setwd("C:/Users/horat/Desktop/CSIROIntership/soilCode")
The working directory was changed to C:/Users/horat/Desktop/CSIROIntership/soilCode inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
library(dplyr)
Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
Attaching package: 愼㸱愼㹥dplyr愼㸱愼㹦
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filter, lag
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intersect, setdiff, setequal, union
#create pivot table
library(reshape)
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rename
library(data.table)
data.table 1.12.8 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com
Attaching package: 愼㸱愼㹥data.table愼㸱愼㹦
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between, first, last
#data partition seperate trainset and testset
library (caTools)
library(caret)
Loading required package: lattice
Loading required package: ggplot2
#svm library due to limitation of iterations change the library
library(e1071)
library(LiblineaR)
#random forest
library(randomForest)
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
Attaching package: 愼㸱愼㹥randomForest愼㸱愼㹦
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margin
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combine
#ID4 Decision Tree classifier(CART)
library(rpart)
library(rpart.plot)
library(rattle)
Rattle: A free graphical interface for data science with R.
XXXX 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
戼㹣昼㹣挼㸸攼㹢'rattle()'ȥ挼㸷攼㸱ҡ愼㸱愼㸲戼㹢ζ愼㹦愼㸱愼㸲戼㸷愼㹤戼㸹昼㸶挼㸴攼㸳戼㸵挼㸴挼㹡昼㹤戼㹥ݡ愼㸳
Attaching package: 愼㸱愼㹥rattle愼㸱愼㹦
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importance
#xgboost
library(xgboost)
Attaching package: 愼㸱愼㹥xgboost愼㸱愼㹦
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#for knn classification
library(class)
Attaching package: 愼㸱愼㹥class愼㸱愼㹦
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#install neuralnetwork
library(neuralnet)
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#adabag library
library(adabag)
Loading required package: foreach
Loading required package: doParallel
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Loading required package: parallel
#Stochastic Gradient Descent (SGD) Method Learning Function
library(gradDescent)
Attaching package: 愼㸱愼㹥gradDescent愼㸱愼㹦
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library(lightgbm)
Loading required package: R6
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#https://www.kaggle.com/c/amazon-employee-access-challenge/discussion/5128#38925
#matrix library
library(Matrix)
Attaching package: 愼㸱愼㹥Matrix愼㸱愼㹦
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#catboost
library(catboost)
#fast naive bayes
library("fastNaiveBayes")
#tidyverse for easy data manipulation and visualization
#caret for easy machine learning workflow
#mlp
library(RSNNS)
Loading required package: Rcpp
Attaching package: 愼㸱愼㹥RSNNS愼㸱愼㹦
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library(tidyverse)
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print.tbl_lazy
print.tbl_sql
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library(caret)
featureSoilTable <- read.csv(file = "featureTable.csv",stringsAsFactors=FALSE)
print(head(featureSoilTable))
normalize <-function(y) {
x<-y[!is.na(y)]
x<-(x - min(x)) / (max(x) - min(x))
y[!is.na(y)]<-x
return(y)
}
#change the NULL to na
featureSoilTable['h_texture'][featureSoilTable['h_texture'] == "NULL"] <- NA
#add appendix to colname to avoid mis-understand of the title of dataframe
colnames(featureSoilTable) <- paste("Str",colnames(featureSoilTable),sep = "_")
print(head(featureSoilTable))
#extract valid and invalid soil sample
validsoilTexture <- featureSoilTable[!is.na(featureSoilTable$Str_h_texture),]
invalidsoilTexture <- featureSoilTable[is.na(featureSoilTable$Str_h_texture),]
# remove columns that only have nas
validsoilTexture <- validsoilTexture[,colSums(is.na(validsoilTexture))<nrow(validsoilTexture)]
#remove rows have less than 4 data
contribution <- as.data.frame(rowsum(rep(1,times = length(validsoilTexture$Str_h_texture)), validsoilTexture$Str_h_texture),row.names = count)
label <- sort(unique(validsoilTexture$Str_h_texture))
contribution <- cbind(label,contribution)
invaliddata <- contribution[contribution$V1 < 4,]
for (l in invaliddata$label){
rowlist = which(validsoilTexture$Str_h_texture == l)
#print(rowlist)
validsoilTexture <- validsoilTexture[-rowlist,]
}
validsoilTexture$Str_h_texture <- as.numeric(as.factor(validsoilTexture$Str_h_texture))
validsoilTexture[,-1] <- apply(apply(validsoilTexture[,-1], 2, as.factor), 2, as.numeric)
validsoilTexture[,-1]<- (apply(validsoilTexture[,-1],2,normalize))
validsoilTexture <- as.data.frame(validsoilTexture)
#change null value to 0
validsoilTexture[is.na(validsoilTexture)] = 0
ncol <- ncol(validsoilTexture)
print(head(validsoilTexture))
set.seed(122)
split = sample.split(validsoilTexture$Str_h_texture,SplitRatio = 0.7)
train_set = subset(validsoilTexture, split == TRUE)
test_set = subset(validsoilTexture, split == FALSE)
train_set$Str_h_texture = as.numeric(train_set$Str_h_texture)
test_set$Str_h_texture = as.numeric(test_set$Str_h_texture)
summary(train_set)
Str_h_texture Str_samp_no Str_labr_no Str_X1.40E.02 Str_X1.40E.04 Str_X1.80E.03 Str_X10_BC
Min. : 1.00 Min. :0.00000 Min. :0.00000 Min. :0.00e+00 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
Str_X10A_NR Str_X10A1 Str_X10B Str_X10B_NR Str_X10B1 Str_X10B3 Str_X10D1
Min. :0.0000000 Min. :0.00e+00 Min. :0.0000000 Min. :0.0e+00 Min. :0.000000 Min. :0.00e+00 Min. :0.000000
Str_X11A1 Str_X12_HCL_CU Str_X12_HCL_FE Str_X12_HCL_MN Str_X12_HCL_ZN Str_X12_HF_CU Str_X12_HF_FE Str_X12_HF_MN
Min. :0.0e+00 Min. :0 Min. :0.00000 Min. :0 Min. :0 Min. :0.000000 Min. :0.000000 Min. :0.0000000
Str_X12_HF_ZN Str_X12_NR_CU Str_X12_NR_FE Str_X12_NR_MN Str_X12_NR_ZN Str_X12_XRF_CU Str_X12_XRF_FE
Min. :0.0000000 Min. :0.0e+00 Min. :0.0000000 Min. :0.00e+00 Min. :0.0000000 Min. :0.000000 Min. :0.0000000
Str_X12_XRF_MN Str_X12_XRF_ZN Str_X12A1_CD Str_X12A1_CO Str_X12A1_Cu Str_X12A1_CU Str_X12A1_Fe Str_X12A1_FE Str_X12A1_Mn
Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0 Min. :0 Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0
Str_X12A1_MN Str_X12A1_PB Str_X12A1_Zn Str_X12A1_ZN Str_X12B1_CU Str_X12B1_ZN Str_X12B2_CD Str_X12B2_CU Str_X12B2_PB
Min. :0.000000 Min. :0 Min. :0 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0 Min. :0
Str_X12B2_ZN Str_X12C1 Str_X12C2 Str_X13_C_FE Str_X13_NR_AL Str_X13_NR_FE Str_X13_NR_MN Str_X13A1_AL
Min. :0 Min. :0.00000 Min. :0.0000000 Min. :0.00e+00 Min. :0.0000000 Min. :0.00e+00 Min. :0.000000 Min. :0.0000000
Str_X13A1_FE Str_X13A1_MN Str_X13A1_SI Str_X13B1_AL Str_X13B1_FE Str_X13C_C_FE Str_X13C1_AL Str_X13C1_FE
Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.00e+00 Min. :0.00e+00 Min. :0 Min. :0.0000000 Min. :0.000000
Str_X13C1_FE203 Str_X13C1_MN Str_X13C1_SI Str_X14_NR_S Str_X140 Str_X14B1 Str_X14C1 Str_X14D1_C
Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0e+00 Min. :0 Min. :0.000000 Min. :0.0000000 Min. :0.00e+00
Str_X14D2_BC Str_X14F1 Str_X14H1_CA Str_X14H1_K Str_X14H1_MG Str_X14H1_NA Str_X15_BASES
Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.00e+00 Min. :0.00e+00 Min. :0.00e+00
Str_X15_HSK_CEC Str_X15_NR Str_X15_NR_AL Str_X15_NR_BSa Str_X15_NR_BSP Str_X15_NR_CA Str_X15_NR_CEC
Min. :0.0000000 Min. :0.00000 Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000
Str_X15_NR_CMR Str_X15_NR_ESP Str_X15_NR_H Str_X15_NR_K Str_X15_NR_MG Str_X15_NR_MN Str_X15_NR_NA
Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.00e+00 Min. :0.0000000
Str_X15A1_CA Str_X15A1_CEC Str_X15A1_K Str_X15A1_MG Str_X15A1_MN Str_X15A1_NA Str_X15A2_CA Str_X15A2_CEC
Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0 Min. :0.0000000 Min. :0.000000 Min. :0.000000
Str_X15A2_K Str_X15A2_MG Str_X15A2_NA Str_X15A3_NA Str_X15B1_CA Str_X15B1_CEC Str_X15B1_K Str_X15B1_MG
Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.00e+00 Min. :0 Min. :0.00e+00 Min. :0.0000000
Str_X15B1_NA Str_X15B2_CA Str_X15B2_CEC Str_X15B2_K Str_X15B2_MG Str_X15B2_NA Str_X15C1_CA
Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000
Str_X15C1_CEC Str_X15C1_K Str_X15C1_MG Str_X15C1_NA Str_X15C1mod_CA Str_X15C1mod_K Str_X15C1mod_MG Str_X15C1mod_NA
Min. :0.000000 Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0 Min. :0 Min. :0 Min. :0
Str_X15C1modCEC Str_X15D1_AL Str_X15D1_CA Str_X15D1_CEC Str_X15D1_K Str_X15D1_MG Str_X15D1_NA
Min. :0 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
Str_X15D2_CA Str_X15D2_CEC Str_X15D2_K Str_X15D2_MG Str_X15D2_NA Str_X15D3_CA Str_X15D3_CEC Str_X15D3_K
Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0 Min. :0
Str_X15D3_MG Str_X15D3_NA Str_X15E1_AL Str_X15E1_CA Str_X15E1_CEC Str_X15E1_H Str_X15E1_K Str_X15E1_MG
Min. :0 Min. :0 Min. :0.000000 Min. :0.000000 Min. :0 Min. :0.0000000 Min. :0.0000000 Min. :0.000000
Str_X15E1_MN Str_X15E1_NA Str_X15E1mod_AL Str_X15E1mod_CA Str_X15E1mod_K Str_X15E1mod_MG Str_X15E1mod_MN Str_X15E1mod_NA
Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
Str_X15E2_CA Str_X15E2_K Str_X15E2_MG Str_X15E2_NA Str_X15E2mod_AL Str_X15E2mod_CA Str_X15E2mod_K Str_X15E2mod_MG
Min. :0.00e+00 Min. :0.00e+00 Min. :0.00e+00 Min. :0.00e+00 Min. :0 Min. :0 Min. :0 Min. :0
Str_X15E2mod_MN Str_X15E2mod_NA Str_X15F1_CA Str_X15F1_CEC Str_X15F1_K Str_X15F1_MG Str_X15F1_NA Str_X15F2
Min. :0 Min. :0 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.0000000
Str_X15F2_AL Str_X15F3 Str_X15F4 Str_X15G_C Str_X15G_C_AL1 Str_X15G_C_AL2 Str_X15G_C_H1 Str_X15G_D
Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0
Str_X15G_H Str_X15G1 Str_X15G1_AL Str_X15G1_H Str_X15I2 Str_X15I3 Str_X15I4 Str_X15J_BASES
Min. :0.00e+00 Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0 Min. :0.0000000 Min. :0.0000000 Min. :0.00000
Str_X15J_C Str_X15J_H Str_X15J1 Str_X15J2_MCLW Str_X15K1 Str_X15L1 Str_X15L1_a Str_X15M1_CMR
Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0 Min. :0 Min. :0.00000 Min. :0.000000 Min. :0
Str_X15M1_K.Mg Str_X15M1AlECEC Str_X15M1CaCEC Str_X15M1CaECEC Str_X15M1KCEC Str_X15M1KECEC Str_X15M1MgCEC Str_X15M1MgECEC Str_X15N1
Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0.000000
Str_X15N1_a Str_X15N1_b Str_X15O1 Str_X17A_HF. Str_X17A_NR Str_X17A1 Str_X17A3_CA Str_X17A3_MG Str_X17A3_NA
Min. :0.000000 Min. :0.000000 Min. :0 Min. :0.0e+00 Min. :0.000000 Min. :0.000000 Min. :0 Min. :0 Min. :0
Str_X17A3_S Str_X17D1_CR Str_X17D1_CU Str_X17D1_FE Str_X17D1_MN Str_X17D1_NI Str_X17D1_PB Str_X17D1_ZN Str_X18_NR Str_X18_NR_K
Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0.0000000 Min. :0.000000
Str_X18A1 Str_X18A1_NR Str_X18A1mod Str_X18B1 Str_X18B2 Str_X18F1_Al Str_X18F1_AL Str_X18F1_As
Min. :0.000000 Min. :0.0000000 Min. :0 Min. :0.00e+00 Min. :0.000000 Min. :0 Min. :0.000000 Min. :0
Str_X18F1_AS Str_X18F1_B Str_X18F1_Ca Str_X18F1_CA Str_X18F1_Cd Str_X18F1_CD Str_X18F1_Co Str_X18F1_CO
Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0 Min. :0.0000000 Min. :0 Min. :0.000000
Str_X18F1_Cu Str_X18F1_CU Str_X18F1_Fe Str_X18F1_FE Str_X18F1_K Str_X18F1_Mg Str_X18F1_MG Str_X18F1_Mn
Min. :0 Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0.000000 Min. :0 Min. :0.0000000 Min. :0
Str_X18F1_MN Str_X18F1_Mo Str_X18F1_MO Str_X18F1_Na Str_X18F1_NA Str_X18F1_Ni Str_X18F1_NI Str_X18F1_P
Min. :0.00000 Min. :0 Min. :0.000000 Min. :0 Min. :0.0000000 Min. :0 Min. :0.0000000 Min. :0.000000
Str_X18F1_Pb Str_X18F1_PB Str_X18F1_S Str_X18F1_Se Str_X18F1_SE Str_X18F1_Zn Str_X18F1_ZN Str_X18I1_CA Str_X18I1_MG
Min. :0 Min. :0.000000 Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0 Min. :0.000000 Min. :0 Min. :0
Str_X18I1_NA Str_X18I1_S Str_X19_COL Str_X19A1 Str_X19B_NR Str_X19B1 Str_X19B2 Str_X19F1 Str_X19F1b
Min. :0 Min. :0 Min. :0.00e+00 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0 Min. :0 Min. :0
Str_X2.00E.01 Str_X2.00E.02 Str_X2_LOI Str_X2A1 Str_X2D1 Str_X2Z1_R1 Str_X2Z1_R2 Str_X2Z2_C
Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.00000 Min. :0.0000000 Min. :0.0000 Min. :0.00000 Min. :0.00000
Str_X2Z2_CLAY Str_X2Z2_CS Str_X2Z2_FS Str_X2Z2_S Str_X2Z2_Z Str_X3_C_B Str_X3_NR Str_X3A_C_2.5
Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.0000000
Str_X3A_TSS Str_X3A1 Str_X4_NR Str_X4A_C_1 Str_X4A_C_2.5 Str_X4A1 Str_X4A1_MCLW Str_X4B_AL
Min. :0.0000000 Min. :0.00e+00 Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.00000 Min. :0 Min. :0.0e+00
Str_X4B_AL_NR Str_X4B_C_2.5 Str_X4B1 Str_X4B2 Str_X4B4 Str_X4B5_MCLW Str_X4C_C_1 Str_X4C1
Min. :0.000000 Min. :0.0000000 Min. :0.00000 Min. :0.00000 Min. :0 Min. :0 Min. :0.000000 Min. :0.000000
Str_X4G_NR Str_X5_C_B Str_X5_NR Str_X5A_C_2.5 Str_X5A_NR Str_X5A1 Str_X5A2 Str_X5A2b
Min. :0.000e+00 Min. :0.0000000 Min. :0.0000000 Min. :0.0e+00 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0
Str_X6_DC Str_X6A1 Str_X6A1_UC Str_X6B1 Str_X6B2 Str_X6B2b Str_X6B3 Str_X6B3a
Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0 Min. :0.000000 Min. :0
Str_X6B3b Str_X6B4_0_30 Str_X6B4_30_100 Str_X6H1_HOC Str_X6H1_POC Str_X6H1_ROC Str_X6H1_TOC Str_X6H2a Str_X6H2b Str_X6H2c Str_X6H3
Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
Str_X6H3_0_30 Str_X6H3_30_100 Str_X6Z Str_X7_C_B Str_X7_NR Str_X7A1 Str_X7A2 Str_X7A2a
Min. :0 Min. :0 Min. :0.00000 Min. :0.0000000 Min. :0.0000000 Min. :0.00000 Min. :0.000000 Min. :0.0000000
Str_X7A5 Str_X7A6b_MCLW Str_X7B1 Str_X7C_1MKCla Str_X7C_1MKClb Str_X7C_CASO4 Str_X7C1 Str_X7C1a
Min. :0.00000 Min. :0 Min. :0.0000000 Min. :0 Min. :0 Min. :0.0000000 Min. :0.0000000 Min. :0.000000
Str_X7C1b Str_X7C1d Str_X7C1e Str_X7C2b Str_X7C2b_NH4 Str_X7C2b_NO3 Str_X7D1a Str_X7E1a Str_X7E1b
Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
Str_X8A1 Str_X9.00E.02 Str_X9_E_NR Str_X9_NR Str_X9A_HCL Str_X9A_HCLP2O5 Str_X9A_HF.
Min. :0.000000 Min. :0.0000000 Min. :0.00e+00 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.00e+00
Str_X9A_NR Str_X9A_S14 Str_X9A1 Str_X9A3 Str_X9A3a Str_X9B Str_X9B_9C Str_X9B_NR
Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0 Min. :0.0000000 Min. :0.000000
Str_X9B1 Str_X9B2 Str_X9B2_COL Str_X9BUFF_0 Str_X9BUFF_0.5 Str_X9BUFF_1 Str_X9BUFF_2
Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
Str_X9BUFF_4 Str_X9C1 Str_X9C2 Str_X9D2 Str_X9E Str_X9G_BSES Str_X9G1 Str_X9G2
Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.00e+00
Str_X9H_NR Str_X9H1 Str_X9I1 Str_X9I2b Str_X9I2B Str_X9J2 Str_X9R1 Str_M1a
Min. :0.00e+00 Min. :0.000000 Min. :0.0000000 Min. :0 Min. :0 Min. :0.000000 Min. :0.000000 Min. :0
Str_MIN_EC Str_MIN_NR_K2O Str_P10_1m2m Str_P10_20_100 Str_P10_20_75 Str_P10_20_75a Str_P10_75_106 Str_P10_C_MCLW
Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.00000 Min. :0.0000000 Min. :0.000000 Min. :0
Str_P10_CF_C Str_P10_CF_CS Str_P10_CF_FS Str_P10_CF_S Str_P10_CF_Z Str_P10_GRAV Str_P10_gt2m Str_P10_gt2MI
Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.000000
Str_P10_gt2OM Str_P10_HYD_C Str_P10_HYD_CS Str_P10_HYD_FS Str_P10_HYD_S Str_P10_HYD_Z Str_P10_I_C Str_P10_I_CS
Min. :0.00e+00 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0 Min. :0.000000 Min. :0 Min. :0
Str_P10_I_FS Str_P10_I_S Str_P10_I_Z Str_P10_NR_C Str_P10_NR_CS Str_P10_NR_FS Str_P10_NR_S Str_P10_NR_Saa Str_P10_NR_Z
Min. :0 Min. :0 Min. :0 Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
Str_P10_NR_ZC Str_P10_PB_C Str_P10_PB_CS Str_P10_PB_FS Str_P10_PB_S Str_P10_PB_Z Str_P10_PB1_C Str_P10_PB1_CS
Min. :0.00e+00 Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.00000 Min. :0.0000000 Min. :0.0000000
Str_P10_PB1_FS Str_P10_PB1_Z Str_P10_S_0.20 Str_P10_S_0.48 Str_P10_S_1 Str_P10_S_1000 Str_P10_S_125
Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.00000 Min. :0.000000
Str_P10_S_15.6 Str_P10_S_2 Str_P10_S_20 Str_P10_S_2000 Str_P10_S_250 Str_P10_S_3.9 Str_P10_S_31.2
Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.0000000
Str_P10_S_500 Str_P10_S_53 Str_P10_S_63 Str_P10_S_7.8 Str_P10_S_MCLW Str_P10_Z_MCLW Str_P10100_200 Str_P10106_150
Min. :0.000000 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0 Min. :0 Min. :0.0000000 Min. :0.000000
Str_P10150_180 Str_P10180_300 Str_P10200_500 Str_P10200_600 Str_P102002000 Str_P10300_600 Str_P105002000
Min. :0.000000 Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0.00e+00
Str_P106001000 Str_P106002000 Str_P10A1_C Str_P10A1_CS Str_P10A1_FS Str_P10A1_Z Str_P3A_CLW Str_P3A_NR
Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0 Min. :0.000000
Str_P3A1 Str_P3A1_C4 Str_P3A1_CLOD Str_P3A1_e Str_P3A2_McK Str_P3A2_McKMP Str_P3B_GV_01 Str_P3B_GV_03
Min. :0.0000 Min. :0.0000000 Min. :0.000000 Min. :0 Min. :0.0000000 Min. :0.000000 Min. :0.00000 Min. :0.0000000
Str_P3B_GV_15 Str_P3B_NR_005 Str_P3B_NR_01 Str_P3B_NR_15 Str_P3B_VL_01 Str_P3B_VL_15 Str_P3B1GV_15
Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.0000000
Str_P3B1VL_1 Str_P3B1VL_15 Str_P3B2GV_1 Str_P3B2GV_15 Str_P3B2GV_5 Str_P3B2VL_03 Str_P3B2VL_1
Min. :0.0000000 Min. :0.0000000 Min. :0.00e+00 Min. :0.00e+00 Min. :0.00e+00 Min. :0.0000000 Min. :0.000000
Str_P3B2VL_15 Str_P3B2VL_5 Str_P3B3VLa001 Str_P3B3VLa005 Str_P3B3VLa01 Str_P3B3VLa03 Str_P3B3VLa06
Min. :0.000000 Min. :0.000000 Min. :0.00e+00 Min. :0.0000000 Min. :0.0000000 Min. :0.00e+00 Min. :0.00e+00
Str_P3B3VLaSAT Str_P3B3VLb001 Str_P3B3VLb003 Str_P3B3VLb005 Str_P3B3VLb01 Str_P3B3VLb03 Str_P3B3VLb05
Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.000000 Min. :0.00000 Min. :0.000000
Str_P3B3VLb06 Str_P3B3VLbSAT Str_P3B3VLc001 Str_P3B3VLc003 Str_P3B3VLc005 Str_P3B3VLc01 Str_P3B3VLc03
Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000
Str_P3B3VLc06 Str_P3B3VLcSAT Str_P3B3VLd06 Str_P3B3VLd1 Str_P3B3VLd15 Str_P3B3VLd3 Str_P3B3VLd5
Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
Str_P3B3VLe004 Str_P3B3VLe01 Str_P3B3VLe03 Str_P3B3VLe06 Str_P3B3VLe15 Str_P3B3VLe2 Str_P3B3VLe7
Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
Str_P3B4GV_01 Str_P3B4VL_005 Str_P3B5GV_01 Str_P3B6VL_DUL Str_P3B6VL_LL Str_P3B6VL_SAT Str_P4_100DMcK Str_P4_10DMcK
Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0 Min. :0 Min. :0.0000000 Min. :0.0000000
Str_P4_30_LOV Str_P4_30DMcK Str_P4_50_McK Str_P4_50DMcK Str_P4_sat Str_P4_sat_FH Str_P4_sat_For Str_P4_sat_LOV
Min. :0.0000000 Min. :0.0e+00 Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0.00e+00 Min. :0.0000000 Min. :0.00000
Str_P4_sat_McK Str_P5_COLE Str_P5_LS_MOD Str_P6_LP Str_PWS1.2mm Str_PWS20.63 Str_PWS212.425
Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.0000000 Min. :0.000000 Min. :0.000000
Str_PWS425.1mm Str_PWS63.212 Str_TE_MIR_AL2O3 Str_TE_MIR_FE2O3 Str_TE_MIR_SI02 Str_TE_NR_AL Str_TE_NR_AL2O Str_TE_NR_CA
Min. :0.000000 Min. :0.000000 Min. :0 Min. :0 Min. :0 Min. :0.0000000 Min. :0.0000000 Min. :0.00e+00
Str_TE_NR_FE20 Str_TE_NR_MG Str_TE_NR_NA Str_TE_NR_SI02 Str_TE_NR_TI02 Str_TE_XRF_MG Str_TE_XRFAL Str_TE_XRFCA
Min. :0.0000000 Min. :0.00e+00 Min. :0.00e+00 Min. :0 Min. :0.00e+00 Min. :0.000000 Min. :0.00e+00 Min. :0.0000000
Str_TE_XRFNA Str_TE_XRFSI02 Str_TE_XRFTIO2 Str_XRD_C_Amp Str_XRD_C_An Str_XRD_C_Bhm Str_XRD_C_Bt Str_XRD_C_Cal
Min. :0.00e+00 Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0.00e+00 Min. :0.00e+00 Min. :0 Min. :0.0000000
Str_XRD_C_Ch2 Str_XRD_C_Chl Str_XRD_C_Fsp Str_XRD_C_Gbs Str_XRD_C_Gth Str_XRD_C_Hem Str_XRD_C_Ht0
Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000 Min. :0.000000 Min. :0
Str_XRD_C_Ilt Str_XRD_C_Is Str_XRD_C_K2O Str_XRD_C_Ka Str_XRD_C_Kln Str_XRD_C_Lp Str_XRD_C_Mag Str_XRD_C_Mca
Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0.00000 Min. :0.0000000 Min. :0.00e+00 Min. :0 Min. :0.0000000
Str_XRD_C_Mgh Str_XRD_C_Mnt Str_XRD_C_Ms Str_XRD_C_Plg Str_XRD_C_Plm Str_XRD_C_Qz Str_XRD_C_Rt Str_XRD_C_Sme
Min. :0.0000000 Min. :0.0000000 Min. :0 Min. :0.00e+00 Min. :0 Min. :0.000000 Min. :0 Min. :0.0000000
Str_XRD_C_Tc Str_XRD_C_Vrm
Min. :0.0e+00 Min. :0.0000000
[ reached getOption("max.print") -- omitted 5 rows ]
# Find the best model with the best cost parameter via 10-fold cross-validations
# the tunning part of svm, which will take lots of time to run
tryTypes=c(0:7)
tryCosts=c(1000,1,0.001)
bestCost=NA
bestAcc=0.6290723
bestType=NA
for(ty in tryTypes){
for(co in tryCosts){
acc=LiblineaR(data=train_set[,-1],target=train_set[,c("Str_h_texture")],type=7,cost=co,bias=1,verbose=FALSE)
cat("Results for C=",co," : ",acc," accuracy.\n",sep="")
if(acc>bestAcc){
bestCost=co
bestAcc=acc
bestType=ty
}
}
}
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized classifiers, L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR).LiblineaR allows the estimation of predictive linear models for classification and regression, such as L1- or L2-regularized logistic regression, L1- or L2-regularized L2-loss support vector classification, L2-regularized L1-loss support vector classification and multi-class support vector classification. It also supports L2-regularized support vector regression (with L1- or L2-loss). The estimation of the models is particularly fast as compared to other libraries.
svmStarttime <- Sys.time()
svmClassifier <- LiblineaR(data = train_set[,-1],target = train_set[,c("Str_h_texture")],bias=1,cost = 1000)
svmPredictTrain <- predict(svmClassifier,train_set[,-1],proba=TRUE,decisionValues=TRUE)
svmPredictTrainTable <- table(svmPredictTrain$predictions,train_set[,c("Str_h_texture")])
svmEndtime <- Sys.time()
svmTimeTaken <- svmEndtime - svmStarttime
svmPredictTest <- predict(svmClassifier,test_set[,-1],proba=TRUE,decisionValues=TRUE)
svmPredictTestTable <- table(svmPredictTest$predictions,test_set[,c("Str_h_texture")])
sumElementinTable <- function(a,c,r){
sum = 0
for (i in c){
if (i %in% r){
sum = sum + a[i,i]
}
}
return(sum)
}
svmTestcol <- colnames(svmPredictTestTable)
svmTestrow <- rownames(svmPredictTestTable)
svmTraincol <- colnames(svmPredictTrainTable)
svmTrainrow <- rownames(svmPredictTrainTable)
svmPredictTestScore <- sumElementinTable(svmPredictTestTable,svmTestcol,svmTestrow)/sum(svmPredictTestTable)
svmPredictTrainScore <- sumElementinTable(svmPredictTrainTable,svmTraincol,svmTrainrow)/sum(svmPredictTrainTable)
# the time of svm is:
cat("the running time of svm is",svmTimeTaken, "seconds")
the running time of svm is 40.66578 seconds
#the score of svm is
cat("The train score of svm algorithm is ",svmPredictTrainScore,'\n')
The train score of svm algorithm is 0.32799
cat("The test score of svm algorithm is ",svmPredictTestScore)
The test score of svm algorithm is 0.3019961
cartFit <- rpart(Str_h_texture ~ .,data = train_set,control = rpart.control(cp = 0.0001))
#get cp value
printcp(cartFit)
Regression tree:
rpart(formula = Str_h_texture ~ ., data = train_set, control = rpart.control(cp = 1e-04))
Variables actually used in tree construction:
[1] Str_MIN_EC Str_P10_1m2m Str_P10_20_75 Str_P10_75_106 Str_P10_CF_C Str_P10_CF_CS Str_P10_CF_FS Str_P10_CF_Z Str_P10_GRAV
[10] Str_P10_HYD_FS Str_P10_NR_C Str_P10_NR_CS Str_P10_NR_FS Str_P10_NR_S Str_P10_NR_Saa Str_P10_NR_Z Str_P10_PB_C Str_P10_PB_CS
[19] Str_P10_PB_FS Str_P10_PB_Z Str_P10_S_15.6 Str_P10106_150 Str_P10150_180 Str_P10180_300 Str_P106001000 Str_P10A1_CS Str_P10A1_FS
[28] Str_P3A_NR Str_P3A1 Str_P3B_GV_15 Str_P3B_NR_15 Str_P3B1GV_15 Str_P3B3VLb001 Str_P3B3VLb003 Str_P3B3VLb005 Str_P3B3VLbSAT
[37] Str_P4_sat_LOV Str_P5_COLE Str_PWS212.425 Str_samp_no Str_X10B1 Str_X10D1 Str_X12_HCL_FE Str_X12_HF_FE Str_X12_NR_FE
[46] Str_X12_XRF_FE Str_X12A1_CU Str_X12A1_FE Str_X12A1_MN Str_X12A1_ZN Str_X12C1 Str_X12C2 Str_X13_C_FE Str_X13_NR_AL
[55] Str_X13_NR_MN Str_X13A1_AL Str_X13A1_FE Str_X13C1_AL Str_X13C1_FE Str_X15_NR Str_X15_NR_AL Str_X15_NR_BSa Str_X15_NR_BSP
[64] Str_X15_NR_CA Str_X15_NR_CEC Str_X15_NR_CMR Str_X15_NR_H Str_X15_NR_K Str_X15_NR_MG Str_X15_NR_NA Str_X15A1_CA Str_X15A1_K
[73] Str_X15A1_MG Str_X15A1_NA Str_X15A2_CA Str_X15A2_CEC Str_X15A2_K Str_X15A2_MG Str_X15C1_CA Str_X15C1_MG Str_X15D1_NA
[82] Str_X15E1_AL Str_X15E1_CA Str_X15E1_K Str_X15E1_MG Str_X15E1_NA Str_X15F1_MG Str_X15F1_NA Str_X15F3 Str_X15G_C
[91] Str_X15G_C_AL1 Str_X15G_C_AL2 Str_X15G_C_H1 Str_X15G1 Str_X15G1_H Str_X15J_BASES Str_X15J_C Str_X15J_H Str_X15J1
[100] Str_X15L1 Str_X15L1_a Str_X15N1 Str_X15N1_b Str_X17A_NR Str_X17A1 Str_X18_NR Str_X18_NR_K Str_X18A1_NR
[109] Str_X18F1_AL Str_X18F1_B Str_X18F1_CA Str_X18F1_CU Str_X18F1_MN Str_X18F1_NA Str_X18F1_NI Str_X19A1 Str_X19B_NR
[118] Str_X2.00E.01 Str_X2_LOI Str_X2A1 Str_X2Z1_R1 Str_X2Z2_C Str_X2Z2_CLAY Str_X2Z2_CS Str_X2Z2_FS Str_X2Z2_S
[127] Str_X2Z2_Z Str_X3_C_B Str_X3_NR Str_X3A_C_2.5 Str_X3A_TSS Str_X3A1 Str_X4_NR Str_X4A_C_2.5 Str_X4A1
[136] Str_X4B_AL_NR Str_X4B_C_2.5 Str_X4B1 Str_X4B2 Str_X4C1 Str_X5_NR Str_X5A1 Str_X5A2 Str_X6A1
[145] Str_X6A1_UC Str_X6B2 Str_X6B3 Str_X6Z Str_X7_NR Str_X7A1 Str_X7A2 Str_X7A5 Str_X7C1a
[154] Str_X7C1b Str_X7C1e Str_X8A1 Str_X9_NR Str_X9A_HCL Str_X9A_NR Str_X9A1 Str_X9A3 Str_X9B_9C
[163] Str_X9B_NR Str_X9B1 Str_X9BUFF_4 Str_X9C2 Str_X9G_BSES Str_X9H1
Root node error: 7040046/47099 = 149.47
n= 47099
CP nsplit rel error xerror xstd
1 0.01055639 0 1.00000 1.00005 0.0057465
2 0.00731024 1 0.98944 0.99031 0.0057947
3 0.00725937 2 0.98213 0.98692 0.0058184
4 0.00525476 3 0.97487 0.97611 0.0058165
5 0.00488331 4 0.96962 0.97144 0.0058483
6 0.00422825 5 0.96474 0.96719 0.0058328
7 0.00336802 7 0.95628 0.96011 0.0058526
8 0.00279386 8 0.95291 0.95659 0.0058604
9 0.00275832 9 0.95012 0.95467 0.0058558
10 0.00237534 10 0.94736 0.95357 0.0058554
11 0.00222392 11 0.94498 0.95042 0.0058583
12 0.00199803 12 0.94276 0.94930 0.0058535
13 0.00156699 13 0.94076 0.94692 0.0058284
14 0.00153254 17 0.93449 0.94416 0.0058518
15 0.00136422 18 0.93296 0.94260 0.0058625
16 0.00128611 19 0.93160 0.94124 0.0058722
17 0.00127887 20 0.93031 0.94105 0.0058758
18 0.00122866 21 0.92903 0.94115 0.0058761
19 0.00113090 24 0.92535 0.93962 0.0058766
20 0.00105562 25 0.92422 0.93801 0.0058831
21 0.00104612 26 0.92316 0.93577 0.0058902
22 0.00103891 27 0.92211 0.93563 0.0058932
23 0.00102448 28 0.92107 0.93527 0.0058903
24 0.00098145 29 0.92005 0.93369 0.0058990
25 0.00097992 30 0.91907 0.93321 0.0059020
26 0.00095101 31 0.91809 0.93306 0.0059028
27 0.00091608 35 0.91428 0.93225 0.0059050
28 0.00091066 36 0.91337 0.93150 0.0059042
29 0.00086168 37 0.91246 0.93036 0.0059026
30 0.00084842 38 0.91160 0.93002 0.0059056
31 0.00082214 39 0.91075 0.92901 0.0059044
32 0.00079626 40 0.90993 0.92788 0.0059012
33 0.00079123 42 0.90833 0.92772 0.0059068
34 0.00077650 43 0.90754 0.92717 0.0059051
35 0.00074354 44 0.90677 0.92676 0.0059114
36 0.00071854 45 0.90602 0.92463 0.0059041
37 0.00070313 48 0.90387 0.92389 0.0059031
38 0.00069515 49 0.90316 0.92336 0.0059010
39 0.00068829 51 0.90177 0.92325 0.0059061
40 0.00068765 53 0.90040 0.92289 0.0059048
41 0.00066500 57 0.89765 0.92156 0.0059007
42 0.00065235 58 0.89698 0.92077 0.0059006
43 0.00061076 59 0.89633 0.91930 0.0059044
44 0.00059984 60 0.89572 0.91762 0.0059039
45 0.00059830 61 0.89512 0.91691 0.0059026
46 0.00059315 62 0.89452 0.91692 0.0059041
47 0.00058856 63 0.89393 0.91678 0.0059039
48 0.00058771 64 0.89334 0.91680 0.0059062
49 0.00058214 65 0.89275 0.91680 0.0059062
50 0.00057935 66 0.89217 0.91663 0.0059091
51 0.00057103 67 0.89159 0.91695 0.0059163
52 0.00056815 68 0.89102 0.91681 0.0059209
53 0.00056678 69 0.89045 0.91678 0.0059229
54 0.00056286 70 0.88988 0.91663 0.0059214
55 0.00053944 71 0.88932 0.91681 0.0059289
56 0.00053652 72 0.88878 0.91662 0.0059322
57 0.00051174 73 0.88824 0.91556 0.0059317
58 0.00050440 74 0.88773 0.91505 0.0059329
59 0.00050140 76 0.88672 0.91501 0.0059330
60 0.00049835 77 0.88622 0.91500 0.0059344
61 0.00049812 78 0.88572 0.91500 0.0059344
62 0.00049244 79 0.88523 0.91509 0.0059362
63 0.00048789 80 0.88473 0.91498 0.0059410
64 0.00047739 81 0.88425 0.91522 0.0059491
65 0.00047291 84 0.88281 0.91456 0.0059492
66 0.00046505 85 0.88234 0.91455 0.0059548
67 0.00046257 86 0.88188 0.91431 0.0059615
68 0.00045308 88 0.88095 0.91382 0.0059651
69 0.00044923 89 0.88050 0.91398 0.0059683
70 0.00043963 91 0.87960 0.91395 0.0059734
71 0.00043842 92 0.87916 0.91432 0.0059800
72 0.00043064 94 0.87828 0.91420 0.0059879
73 0.00042962 96 0.87742 0.91458 0.0060036
74 0.00042790 97 0.87699 0.91467 0.0060052
75 0.00042686 98 0.87656 0.91467 0.0060052
76 0.00042243 99 0.87614 0.91495 0.0060100
77 0.00041790 100 0.87571 0.91523 0.0060162
78 0.00041284 101 0.87530 0.91544 0.0060276
79 0.00041211 102 0.87488 0.91545 0.0060310
80 0.00041042 103 0.87447 0.91540 0.0060313
81 0.00039957 104 0.87406 0.91546 0.0060358
82 0.00039822 105 0.87366 0.91528 0.0060397
83 0.00039635 108 0.87247 0.91526 0.0060425
84 0.00039492 109 0.87207 0.91540 0.0060434
85 0.00039134 110 0.87168 0.91573 0.0060462
86 0.00039017 111 0.87128 0.91548 0.0060459
87 0.00038810 112 0.87089 0.91566 0.0060483
88 0.00038620 113 0.87051 0.91567 0.0060508
89 0.00038609 114 0.87012 0.91575 0.0060509
90 0.00038032 115 0.86973 0.91588 0.0060637
91 0.00037442 116 0.86935 0.91584 0.0060698
92 0.00037052 117 0.86898 0.91557 0.0060719
93 0.00036813 126 0.86560 0.91571 0.0060748
94 0.00036370 127 0.86524 0.91600 0.0060802
95 0.00036352 128 0.86487 0.91576 0.0060851
96 0.00036068 130 0.86414 0.91597 0.0060883
97 0.00035767 132 0.86342 0.91568 0.0060891
98 0.00035750 133 0.86307 0.91564 0.0060889
99 0.00035398 135 0.86235 0.91555 0.0060918
100 0.00035194 138 0.86129 0.91577 0.0060988
101 0.00034990 139 0.86094 0.91559 0.0060995
102 0.00034948 140 0.86059 0.91559 0.0061019
103 0.00034676 141 0.86024 0.91527 0.0061011
104 0.00034622 142 0.85989 0.91519 0.0061034
105 0.00034456 147 0.85816 0.91519 0.0061034
106 0.00034246 151 0.85672 0.91536 0.0061096
107 0.00033662 152 0.85638 0.91556 0.0061171
108 0.00033395 153 0.85604 0.91564 0.0061334
109 0.00033268 154 0.85571 0.91567 0.0061383
110 0.00032887 155 0.85537 0.91537 0.0061363
111 0.00032572 156 0.85504 0.91557 0.0061469
112 0.00032510 158 0.85439 0.91615 0.0061493
113 0.00031947 159 0.85407 0.91655 0.0061584
114 0.00031775 161 0.85343 0.91695 0.0061688
115 0.00031593 162 0.85311 0.91720 0.0061729
116 0.00031328 164 0.85248 0.91698 0.0061741
117 0.00031156 165 0.85217 0.91735 0.0061766
118 0.00031037 166 0.85185 0.91726 0.0061757
119 0.00030824 167 0.85154 0.91756 0.0061783
120 0.00030795 168 0.85124 0.91768 0.0061822
121 0.00030772 169 0.85093 0.91782 0.0061877
122 0.00030690 171 0.85031 0.91794 0.0061885
123 0.00030509 172 0.85000 0.91800 0.0061903
124 0.00030499 173 0.84970 0.91780 0.0061904
125 0.00030336 177 0.84844 0.91831 0.0061949
126 0.00030219 178 0.84814 0.91845 0.0061984
127 0.00030110 180 0.84754 0.91839 0.0061997
128 0.00029637 181 0.84723 0.91852 0.0061982
129 0.00029529 182 0.84694 0.91876 0.0062019
130 0.00028994 183 0.84664 0.91813 0.0061992
131 0.00028817 184 0.84635 0.91839 0.0062084
132 0.00028717 185 0.84606 0.91837 0.0062119
133 0.00028586 186 0.84578 0.91845 0.0062140
134 0.00028383 187 0.84549 0.91877 0.0062201
135 0.00028206 188 0.84521 0.91889 0.0062257
136 0.00028135 189 0.84493 0.91936 0.0062305
137 0.00027903 190 0.84464 0.91931 0.0062316
138 0.00027870 193 0.84381 0.91941 0.0062336
139 0.00027781 194 0.84353 0.91950 0.0062338
140 0.00027734 195 0.84325 0.91954 0.0062346
141 0.00027728 198 0.84242 0.91959 0.0062349
142 0.00027542 204 0.84075 0.91953 0.0062346
143 0.00027396 205 0.84048 0.91941 0.0062400
144 0.00027196 207 0.83993 0.91916 0.0062405
145 0.00027078 208 0.83966 0.91927 0.0062438
146 0.00026925 209 0.83939 0.91911 0.0062464
147 0.00026799 210 0.83912 0.91938 0.0062486
148 0.00026768 211 0.83885 0.91938 0.0062480
149 0.00026690 213 0.83832 0.91930 0.0062456
150 0.00026672 214 0.83805 0.91955 0.0062497
151 0.00026547 215 0.83778 0.91956 0.0062504
152 0.00026466 217 0.83725 0.91946 0.0062521
153 0.00026401 218 0.83699 0.91944 0.0062520
154 0.00026229 219 0.83672 0.91974 0.0062546
155 0.00025761 222 0.83594 0.92056 0.0062678
156 0.00025719 225 0.83516 0.92028 0.0062717
157 0.00025523 226 0.83491 0.92069 0.0062754
158 0.00025383 227 0.83465 0.92088 0.0062780
159 0.00025273 228 0.83440 0.92084 0.0062796
160 0.00025248 229 0.83414 0.92092 0.0062820
161 0.00025215 232 0.83339 0.92110 0.0062838
162 0.00025124 233 0.83313 0.92106 0.0062854
163 0.00025104 234 0.83288 0.92150 0.0062901
164 0.00025047 235 0.83263 0.92149 0.0062901
165 0.00024911 238 0.83188 0.92214 0.0062948
166 0.00024848 239 0.83163 0.92198 0.0062953
167 0.00024746 241 0.83113 0.92165 0.0062926
168 0.00024631 242 0.83089 0.92145 0.0062927
169 0.00024584 243 0.83064 0.92167 0.0062962
170 0.00024321 244 0.83040 0.92208 0.0063006
171 0.00024219 245 0.83015 0.92219 0.0063082
172 0.00024212 246 0.82991 0.92251 0.0063106
173 0.00024193 248 0.82943 0.92280 0.0063128
174 0.00024139 253 0.82810 0.92314 0.0063178
175 0.00024093 254 0.82786 0.92331 0.0063212
176 0.00024060 255 0.82761 0.92329 0.0063215
177 0.00024050 256 0.82737 0.92331 0.0063217
178 0.00024007 263 0.82566 0.92337 0.0063230
179 0.00023873 264 0.82542 0.92324 0.0063234
180 0.00023594 265 0.82518 0.92277 0.0063227
181 0.00023546 268 0.82447 0.92304 0.0063255
182 0.00023471 269 0.82423 0.92319 0.0063268
183 0.00023392 270 0.82400 0.92375 0.0063338
184 0.00023384 271 0.82376 0.92388 0.0063341
185 0.00022993 272 0.82353 0.92477 0.0063461
186 0.00022965 274 0.82307 0.92484 0.0063511
187 0.00022961 278 0.82212 0.92495 0.0063537
188 0.00022844 279 0.82189 0.92514 0.0063584
189 0.00022781 282 0.82121 0.92542 0.0063622
190 0.00022772 283 0.82098 0.92529 0.0063625
191 0.00022729 284 0.82075 0.92535 0.0063623
192 0.00022669 285 0.82053 0.92530 0.0063628
193 0.00022647 290 0.81937 0.92568 0.0063689
194 0.00022560 291 0.81914 0.92592 0.0063720
195 0.00022489 292 0.81891 0.92580 0.0063762
196 0.00022486 296 0.81802 0.92640 0.0063835
197 0.00022186 297 0.81779 0.92740 0.0063932
198 0.00022123 302 0.81654 0.92744 0.0063966
199 0.00021997 303 0.81632 0.92731 0.0063971
200 0.00021967 304 0.81610 0.92759 0.0063993
[ reached getOption("max.print") -- omitted 314 rows ]
choose the CP with lowest xerror
cartstartTime <- Sys.time()
fit.pruned = prune(cartFit, cp = 0.00021967)
cartPrediction <- predict(fit.pruned, test_set, type = "vector")
cartendTime <- Sys.time()
cartTimeTaken <- cartendTime - cartstartTime
data.frame(test_set,cartPrediction)
cartPrediction = round(cartPrediction,0)
cartTable <- table(test_set$Str_h_texture,cartPrediction)
cartTable
cartPrediction
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 3 0 0 0 0 0
2 0 0 0 0 1 4 0 4 0 0 0 0 0 0 1 5 2 0 3 0 1 0 3 25 0 0 3 1 0
3 0 0 0 1 1 0 5 0 2 1 0 0 1 1 4 8 2 0 4 7 0 4 4 51 3 7 3 0 2
4 11 2 17 38 20 9 7 0 11 36 39 2 25 35 19 15 16 12 20 47 29 21 38 487 28 40 28 15 21
5 0 0 0 4 2 0 0 0 7 2 16 0 1 4 0 5 0 0 1 2 1 0 4 75 2 6 7 2 3
6 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 8 0 1 3 1 2
7 0 0 0 0 1 0 0 1 2 3 7 2 1 1 8 2 2 1 0 3 9 5 5 153 3 6 4 6 2
8 0 0 0 0 1 1 6 13 12 2 2 10 0 4 1 13 10 0 14 5 5 9 22 265 6 43 11 18 12
9 0 0 0 0 0 3 2 9 1 4 0 0 0 5 1 17 3 0 14 6 18 0 3 92 4 7 4 11 0
10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 4 0 0 0 0 0
11 0 1 1 1 13 0 0 1 11 2 23 0 6 14 1 5 0 2 0 8 7 11 10 139 3 12 5 0 3
12 0 0 0 8 1 0 0 4 7 16 50 0 5 28 8 11 9 2 9 12 15 9 33 172 16 12 8 7 7
13 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 2 0 0 0 1 0 5 0 49 1 16 4 0 0
14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 2 0 0 1
15 0 0 0 1 0 0 0 0 0 2 0 0 1 2 1 1 1 0 2 0 2 1 2 38 1 5 8 2 2
16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
17 1 0 0 1 3 0 1 0 0 3 0 0 22 29 38 75 23 31 77 115 96 29 54 453 17 170 36 5 6
18 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0
19 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 8 1 5 7 5 2 2 93 6 15 3 9 5
20 0 0 0 0 0 0 1 3 2 3 4 0 9 1 4 16 3 1 6 7 0 5 8 36 1 4 17 2 1
21 0 0 0 0 0 0 1 0 0 2 3 1 5 3 3 6 0 0 2 3 2 8 3 37 10 11 12 1 4
22 0 0 0 1 0 0 0 0 0 1 0 0 0 0 3 10 1 0 0 0 0 1 4 29 1 4 2 0 0
23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 1 2 0 1
cartPrediction
35 36 37 38 39 40 41 42 43 47 48 49 50 52
1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 2 1 0 0 0 0 0 0 0 0 0 0 0 0
4 13 6 12 14 1 2 3 1 0 2 0 0 0 0
5 2 1 0 1 0 0 0 0 0 0 0 0 0 0
6 5 2 0 3 0 0 0 0 0 0 0 0 0 0
7 4 5 0 2 4 0 0 0 0 0 0 0 0 0
8 5 4 2 9 4 0 1 0 0 0 0 0 0 0
9 4 2 0 2 1 0 1 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11 2 0 0 0 5 0 0 0 0 0 0 0 0 0
12 3 4 1 5 0 0 2 0 0 0 0 1 0 0
13 0 0 2 0 0 0 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 0 0 0 0 0
15 6 1 0 1 0 0 0 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0 0 0 0 0 0 0 0
17 7 2 1 6 1 0 1 2 0 1 0 0 0 0
18 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 7 7 0 1 0 0 0 0 0 0 0 0 0 0
20 1 5 0 1 1 0 0 0 0 0 0 0 0 0
21 0 1 0 6 3 0 0 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23 1 0 0 0 0 0 0 0 0 0 0 0 0 0
[ reached getOption("max.print") -- omitted 31 rows ]
calculate the score of cart model
cartrow <- rownames(cartTable)
cartcol <- colnames(cartTable)
cartscore <- sumElementinTable(cartTable,cartrow,cartcol)/sum(cartTable)
the time of cart model
cat("the time of cart",cartTimeTaken , "seconds")
the time of cart 0.320148 seconds
the score of cart model
cat('the score of cart model',cartscore)
the score of cart model 0.02476596
separate x and y from train_set and test_set
train_set.num_X <- select (train_set,-c(Str_h_texture))
test_set.num_X <- select (test_set,-c(Str_h_texture))
start lightgbm machine learning algorithms
lstarttime <- Sys.time()
ltrain = lgb.Dataset(data = as.matrix(train_set.num_X),label = train_set$Str_h_texture, free_raw_data = FALSE)
params <- list(objective="regression", metric="l2")
model <- lgb.cv(params,
ltrain ,
10,
nfold=5,
min_data=1,
learning_rate=1,
early_stopping_rounds=10,
Depth = 8,
lambda_l1 = 10,
lambda_l2 = 10
)
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046899 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 37680, number of used features: 451
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 37679, number of used features: 451
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033684 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 37679, number of used features: 451
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029977 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 37679, number of used features: 451
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 37679, number of used features: 451
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Info] Start training from score 29.139066
[LightGBM] [Info] Start training from score 29.136575
[LightGBM] [Info] Start training from score 29.121288
[LightGBM] [Info] Start training from score 29.096473
[LightGBM] [Info] Start training from score 29.103532
[1]: valid's l2:139.488+2.86001
[2]: valid's l2:137.484+2.48886
[3]: valid's l2:136.646+2.49735
[4]: valid's l2:135.815+2.72855
[5]: valid's l2:135.377+2.73764
[6]: valid's l2:135.16+2.75332
[7]: valid's l2:134.94+2.76135
[8]: valid's l2:134.732+2.82976
[9]: valid's l2:134.646+2.70722
[10]: valid's l2:134.342+2.72809
lstoptime <- Sys.time()
num_leaves: This is the main parameter to control the complexity of the tree model. Theoretically, we can set num_leaves = 2^(max_depth) to obtain the same number of leaves as depth-wise tree. However, this simple conversion is not good in practice. The reason is that a leaf-wise tree is typically much deeper than a depth-wise tree for a fixed number of leaves. Unconstrained depth can induce over-fitting. Thus, when trying to tune the num_leaves, we should let it be smaller than 2^(max_depth). For example, when the max_depth=7 the depth-wise tree can get good accuracy, but setting num_leaves to 127 may cause over-fitting, and setting it to 70 or 80 may get better accuracy than depth-wise.
min_data_in_leaf: This is a very important parameter to prevent over-fitting in a leaf-wise tree. Its optimal value depends on the number of training samples and num_leaves. Setting it to a large value can avoid growing too deep a tree, but may cause under-fitting. In practice, setting it to hundreds or thousands is enough for a large dataset.
max_depth: You also can use max_depth to limit the tree depth explicitly.
ltest = lgb.Dataset.create.valid(ltrain , as.matrix(test_set.num_X), label = test_set$Str_h_texture)
valids <- list(test = ltest)
grid_search <- expand.grid(Depth = 7:8,
L1 = 8:12,
L2 = 8:12)
model <- list()
perf <- numeric(nrow(grid_search))
for (i in 1:nrow(grid_search)) {
model[[i]] <- lgb.train(list(objective = "regression",
metric = "l2",
lambda_l1 = grid_search[i, "L1"],
lambda_l2 = grid_search[i, "L2"],
max_depth = grid_search[i, "Depth"]),
ltrain,
2,
valids,
min_data = 1,
learning_rate = 1,
early_stopping_rounds = 5,
num_leaves = 2,
num_iterations = 100,
min_gain_to_split = 500,)
perf[i] <- min(rbindlist(model[[i]]$record_evals$test$l2))
}
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040525 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.803
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.309
[10]: test's l2:143.032
[11]: test's l2:142.69
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.731
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.947
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.392
[25]: test's l2:140.275
[26]: test's l2:140.16
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.684
[31]: test's l2:139.527
[32]: test's l2:139.458
[33]: test's l2:139.465
[34]: test's l2:139.358
[35]: test's l2:139.331
[36]: test's l2:139.258
[37]: test's l2:139.133
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.583
[51]: test's l2:138.557
[52]: test's l2:138.537
[53]: test's l2:138.529
[54]: test's l2:138.457
[55]: test's l2:138.449
[56]: test's l2:138.366
[57]: test's l2:138.264
[58]: test's l2:138.244
[59]: test's l2:138.217
[60]: test's l2:138.126
[61]: test's l2:138.063
[62]: test's l2:138.062
[63]: test's l2:138.031
[64]: test's l2:137.99
[65]: test's l2:137.93
[66]: test's l2:137.852
[67]: test's l2:137.758
[68]: test's l2:137.726
[69]: test's l2:137.723
[70]: test's l2:137.619
[71]: test's l2:137.611
[72]: test's l2:137.545
[73]: test's l2:137.496
[74]: test's l2:137.526
[75]: test's l2:137.488
[76]: test's l2:137.439
[77]: test's l2:137.403
[78]: test's l2:137.425
[79]: test's l2:137.324
[80]: test's l2:137.322
[81]: test's l2:137.287
[82]: test's l2:137.331
[83]: test's l2:137.34
[84]: test's l2:137.363
[85]: test's l2:137.294
[86]: test's l2:137.284
[87]: test's l2:137.278
[88]: test's l2:137.211
[89]: test's l2:137.233
[90]: test's l2:137.135
[91]: test's l2:137.131
[92]: test's l2:137.145
[93]: test's l2:137.154
[94]: test's l2:137.099
[95]: test's l2:137.027
[96]: test's l2:137.047
[97]: test's l2:136.998
[98]: test's l2:137.011
[99]: test's l2:136.952
[100]: test's l2:136.973
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059030 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.803
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.309
[10]: test's l2:143.032
[11]: test's l2:142.69
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.731
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.947
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.392
[25]: test's l2:140.275
[26]: test's l2:140.16
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.684
[31]: test's l2:139.527
[32]: test's l2:139.458
[33]: test's l2:139.465
[34]: test's l2:139.358
[35]: test's l2:139.331
[36]: test's l2:139.258
[37]: test's l2:139.133
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.583
[51]: test's l2:138.557
[52]: test's l2:138.537
[53]: test's l2:138.529
[54]: test's l2:138.457
[55]: test's l2:138.449
[56]: test's l2:138.366
[57]: test's l2:138.264
[58]: test's l2:138.244
[59]: test's l2:138.217
[60]: test's l2:138.126
[61]: test's l2:138.063
[62]: test's l2:138.062
[63]: test's l2:138.031
[64]: test's l2:137.99
[65]: test's l2:137.93
[66]: test's l2:137.852
[67]: test's l2:137.758
[68]: test's l2:137.726
[69]: test's l2:137.723
[70]: test's l2:137.619
[71]: test's l2:137.611
[72]: test's l2:137.545
[73]: test's l2:137.496
[74]: test's l2:137.526
[75]: test's l2:137.488
[76]: test's l2:137.439
[77]: test's l2:137.403
[78]: test's l2:137.425
[79]: test's l2:137.324
[80]: test's l2:137.322
[81]: test's l2:137.287
[82]: test's l2:137.331
[83]: test's l2:137.34
[84]: test's l2:137.363
[85]: test's l2:137.294
[86]: test's l2:137.284
[87]: test's l2:137.278
[88]: test's l2:137.211
[89]: test's l2:137.233
[90]: test's l2:137.135
[91]: test's l2:137.131
[92]: test's l2:137.145
[93]: test's l2:137.154
[94]: test's l2:137.099
[95]: test's l2:137.027
[96]: test's l2:137.047
[97]: test's l2:136.998
[98]: test's l2:137.011
[99]: test's l2:136.952
[100]: test's l2:136.973
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.041312 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.803
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.69
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.16
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.684
[31]: test's l2:139.528
[32]: test's l2:139.458
[33]: test's l2:139.465
[34]: test's l2:139.358
[35]: test's l2:139.332
[36]: test's l2:139.258
[37]: test's l2:139.133
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.558
[51]: test's l2:138.538
[52]: test's l2:138.537
[53]: test's l2:138.529
[54]: test's l2:138.457
[55]: test's l2:138.356
[56]: test's l2:138.335
[57]: test's l2:138.327
[58]: test's l2:138.244
[59]: test's l2:138.217
[60]: test's l2:138.126
[61]: test's l2:138.063
[62]: test's l2:138.062
[63]: test's l2:138.031
[64]: test's l2:137.99
[65]: test's l2:137.931
[66]: test's l2:137.852
[67]: test's l2:137.758
[68]: test's l2:137.726
[69]: test's l2:137.724
[70]: test's l2:137.62
[71]: test's l2:137.612
[72]: test's l2:137.545
[73]: test's l2:137.496
[74]: test's l2:137.527
[75]: test's l2:137.488
[76]: test's l2:137.439
[77]: test's l2:137.403
[78]: test's l2:137.425
[79]: test's l2:137.325
[80]: test's l2:137.322
[81]: test's l2:137.288
[82]: test's l2:137.331
[83]: test's l2:137.34
[84]: test's l2:137.363
[85]: test's l2:137.294
[86]: test's l2:137.284
[87]: test's l2:137.278
[88]: test's l2:137.211
[89]: test's l2:137.233
[90]: test's l2:137.135
[91]: test's l2:137.131
[92]: test's l2:137.145
[93]: test's l2:137.153
[94]: test's l2:137.099
[95]: test's l2:137.027
[96]: test's l2:137.047
[97]: test's l2:136.998
[98]: test's l2:137.01
[99]: test's l2:136.951
[100]: test's l2:136.972
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.057814 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.803
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.69
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.16
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.684
[31]: test's l2:139.528
[32]: test's l2:139.458
[33]: test's l2:139.465
[34]: test's l2:139.358
[35]: test's l2:139.332
[36]: test's l2:139.258
[37]: test's l2:139.133
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.558
[51]: test's l2:138.538
[52]: test's l2:138.537
[53]: test's l2:138.529
[54]: test's l2:138.457
[55]: test's l2:138.356
[56]: test's l2:138.335
[57]: test's l2:138.327
[58]: test's l2:138.244
[59]: test's l2:138.217
[60]: test's l2:138.126
[61]: test's l2:138.063
[62]: test's l2:138.062
[63]: test's l2:138.031
[64]: test's l2:137.99
[65]: test's l2:137.931
[66]: test's l2:137.852
[67]: test's l2:137.758
[68]: test's l2:137.726
[69]: test's l2:137.724
[70]: test's l2:137.62
[71]: test's l2:137.612
[72]: test's l2:137.545
[73]: test's l2:137.496
[74]: test's l2:137.527
[75]: test's l2:137.488
[76]: test's l2:137.439
[77]: test's l2:137.403
[78]: test's l2:137.425
[79]: test's l2:137.325
[80]: test's l2:137.322
[81]: test's l2:137.288
[82]: test's l2:137.331
[83]: test's l2:137.34
[84]: test's l2:137.363
[85]: test's l2:137.294
[86]: test's l2:137.284
[87]: test's l2:137.278
[88]: test's l2:137.211
[89]: test's l2:137.233
[90]: test's l2:137.135
[91]: test's l2:137.131
[92]: test's l2:137.145
[93]: test's l2:137.153
[94]: test's l2:137.099
[95]: test's l2:137.027
[96]: test's l2:137.047
[97]: test's l2:136.998
[98]: test's l2:137.01
[99]: test's l2:136.951
[100]: test's l2:136.972
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.052319 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.803
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.161
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.684
[31]: test's l2:139.528
[32]: test's l2:139.459
[33]: test's l2:139.466
[34]: test's l2:139.359
[35]: test's l2:139.332
[36]: test's l2:139.259
[37]: test's l2:139.133
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.558
[51]: test's l2:138.537
[52]: test's l2:138.536
[53]: test's l2:138.528
[54]: test's l2:138.457
[55]: test's l2:138.356
[56]: test's l2:138.335
[57]: test's l2:138.327
[58]: test's l2:138.243
[59]: test's l2:138.216
[60]: test's l2:138.126
[61]: test's l2:138.063
[62]: test's l2:138.062
[63]: test's l2:138.031
[64]: test's l2:137.99
[65]: test's l2:137.93
[66]: test's l2:137.852
[67]: test's l2:137.758
[68]: test's l2:137.726
[69]: test's l2:137.723
[70]: test's l2:137.619
[71]: test's l2:137.611
[72]: test's l2:137.545
[73]: test's l2:137.496
[74]: test's l2:137.526
[75]: test's l2:137.488
[76]: test's l2:137.439
[77]: test's l2:137.403
[78]: test's l2:137.424
[79]: test's l2:137.324
[80]: test's l2:137.322
[81]: test's l2:137.288
[82]: test's l2:137.33
[83]: test's l2:137.339
[84]: test's l2:137.362
[85]: test's l2:137.294
[86]: test's l2:137.284
[87]: test's l2:137.277
[88]: test's l2:137.21
[89]: test's l2:137.232
[90]: test's l2:137.134
[91]: test's l2:137.13
[92]: test's l2:137.144
[93]: test's l2:137.152
[94]: test's l2:137.098
[95]: test's l2:137.026
[96]: test's l2:137.046
[97]: test's l2:136.996
[98]: test's l2:137.009
[99]: test's l2:136.95
[100]: test's l2:136.971
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040858 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.803
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.161
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.684
[31]: test's l2:139.528
[32]: test's l2:139.459
[33]: test's l2:139.466
[34]: test's l2:139.359
[35]: test's l2:139.332
[36]: test's l2:139.259
[37]: test's l2:139.133
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.558
[51]: test's l2:138.537
[52]: test's l2:138.536
[53]: test's l2:138.528
[54]: test's l2:138.457
[55]: test's l2:138.356
[56]: test's l2:138.335
[57]: test's l2:138.327
[58]: test's l2:138.243
[59]: test's l2:138.216
[60]: test's l2:138.126
[61]: test's l2:138.063
[62]: test's l2:138.062
[63]: test's l2:138.031
[64]: test's l2:137.99
[65]: test's l2:137.93
[66]: test's l2:137.852
[67]: test's l2:137.758
[68]: test's l2:137.726
[69]: test's l2:137.723
[70]: test's l2:137.619
[71]: test's l2:137.611
[72]: test's l2:137.545
[73]: test's l2:137.496
[74]: test's l2:137.526
[75]: test's l2:137.488
[76]: test's l2:137.439
[77]: test's l2:137.403
[78]: test's l2:137.424
[79]: test's l2:137.324
[80]: test's l2:137.322
[81]: test's l2:137.288
[82]: test's l2:137.33
[83]: test's l2:137.339
[84]: test's l2:137.362
[85]: test's l2:137.294
[86]: test's l2:137.284
[87]: test's l2:137.277
[88]: test's l2:137.21
[89]: test's l2:137.232
[90]: test's l2:137.134
[91]: test's l2:137.13
[92]: test's l2:137.144
[93]: test's l2:137.152
[94]: test's l2:137.098
[95]: test's l2:137.026
[96]: test's l2:137.046
[97]: test's l2:136.996
[98]: test's l2:137.009
[99]: test's l2:136.95
[100]: test's l2:136.971
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038173 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.161
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.685
[31]: test's l2:139.529
[32]: test's l2:139.459
[33]: test's l2:139.466
[34]: test's l2:139.359
[35]: test's l2:139.332
[36]: test's l2:139.259
[37]: test's l2:139.134
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.558
[51]: test's l2:138.537
[52]: test's l2:138.536
[53]: test's l2:138.528
[54]: test's l2:138.425
[55]: test's l2:138.405
[56]: test's l2:138.334
[57]: test's l2:138.307
[58]: test's l2:138.299
[59]: test's l2:138.214
[60]: test's l2:138.151
[61]: test's l2:138.061
[62]: test's l2:138.06
[63]: test's l2:138.029
[64]: test's l2:137.988
[65]: test's l2:137.929
[66]: test's l2:137.85
[67]: test's l2:137.757
[68]: test's l2:137.724
[69]: test's l2:137.722
[70]: test's l2:137.618
[71]: test's l2:137.61
[72]: test's l2:137.543
[73]: test's l2:137.494
[74]: test's l2:137.525
[75]: test's l2:137.5
[76]: test's l2:137.436
[77]: test's l2:137.458
[78]: test's l2:137.42
[79]: test's l2:137.321
[80]: test's l2:137.319
[81]: test's l2:137.284
[82]: test's l2:137.327
[83]: test's l2:137.336
[84]: test's l2:137.359
[85]: test's l2:137.29
[86]: test's l2:137.28
[87]: test's l2:137.274
[88]: test's l2:137.207
[89]: test's l2:137.229
[90]: test's l2:137.131
[91]: test's l2:137.127
[92]: test's l2:137.141
[93]: test's l2:137.087
[94]: test's l2:137.016
[95]: test's l2:137.024
[96]: test's l2:137.044
[97]: test's l2:136.995
[98]: test's l2:137.007
[99]: test's l2:136.949
[100]: test's l2:136.969
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.057746 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.302
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.161
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.685
[31]: test's l2:139.529
[32]: test's l2:139.459
[33]: test's l2:139.466
[34]: test's l2:139.359
[35]: test's l2:139.332
[36]: test's l2:139.259
[37]: test's l2:139.134
[38]: test's l2:139.152
[39]: test's l2:139.175
[40]: test's l2:139.04
[41]: test's l2:139.026
[42]: test's l2:138.988
[43]: test's l2:138.883
[44]: test's l2:138.839
[45]: test's l2:138.788
[46]: test's l2:138.749
[47]: test's l2:138.755
[48]: test's l2:138.624
[49]: test's l2:138.583
[50]: test's l2:138.558
[51]: test's l2:138.537
[52]: test's l2:138.536
[53]: test's l2:138.528
[54]: test's l2:138.425
[55]: test's l2:138.405
[56]: test's l2:138.334
[57]: test's l2:138.307
[58]: test's l2:138.299
[59]: test's l2:138.214
[60]: test's l2:138.151
[61]: test's l2:138.061
[62]: test's l2:138.06
[63]: test's l2:138.029
[64]: test's l2:137.988
[65]: test's l2:137.929
[66]: test's l2:137.85
[67]: test's l2:137.757
[68]: test's l2:137.724
[69]: test's l2:137.722
[70]: test's l2:137.618
[71]: test's l2:137.61
[72]: test's l2:137.543
[73]: test's l2:137.494
[74]: test's l2:137.525
[75]: test's l2:137.5
[76]: test's l2:137.436
[77]: test's l2:137.458
[78]: test's l2:137.42
[79]: test's l2:137.321
[80]: test's l2:137.319
[81]: test's l2:137.284
[82]: test's l2:137.327
[83]: test's l2:137.336
[84]: test's l2:137.359
[85]: test's l2:137.29
[86]: test's l2:137.28
[87]: test's l2:137.274
[88]: test's l2:137.207
[89]: test's l2:137.229
[90]: test's l2:137.131
[91]: test's l2:137.127
[92]: test's l2:137.141
[93]: test's l2:137.087
[94]: test's l2:137.016
[95]: test's l2:137.024
[96]: test's l2:137.044
[97]: test's l2:136.995
[98]: test's l2:137.007
[99]: test's l2:136.949
[100]: test's l2:136.969
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.060413 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.161
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.685
[31]: test's l2:139.529
[32]: test's l2:139.459
[33]: test's l2:139.466
[34]: test's l2:139.359
[35]: test's l2:139.332
[36]: test's l2:139.259
[37]: test's l2:139.134
[38]: test's l2:139.153
[39]: test's l2:139.175
[40]: test's l2:139.146
[41]: test's l2:139.063
[42]: test's l2:138.928
[43]: test's l2:138.823
[44]: test's l2:138.827
[45]: test's l2:138.785
[46]: test's l2:138.734
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.525
[50]: test's l2:138.499
[51]: test's l2:138.478
[52]: test's l2:138.469
[53]: test's l2:138.364
[54]: test's l2:138.343
[55]: test's l2:138.342
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.088
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.887
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.58
[68]: test's l2:137.55
[69]: test's l2:137.511
[70]: test's l2:137.514
[71]: test's l2:137.474
[72]: test's l2:137.475
[73]: test's l2:137.436
[74]: test's l2:137.388
[75]: test's l2:137.353
[76]: test's l2:137.375
[77]: test's l2:137.326
[78]: test's l2:137.357
[79]: test's l2:137.26
[80]: test's l2:137.195
[81]: test's l2:137.139
[82]: test's l2:137.137
[83]: test's l2:137.101
[84]: test's l2:137.143
[85]: test's l2:137.136
[86]: test's l2:137.086
[87]: test's l2:137.091
[88]: test's l2:137.014
[89]: test's l2:137.035
[90]: test's l2:137.042
[91]: test's l2:136.976
[92]: test's l2:136.921
[93]: test's l2:136.931
[94]: test's l2:136.873
[95]: test's l2:136.827
[96]: test's l2:136.777
[97]: test's l2:136.774
[98]: test's l2:136.725
[99]: test's l2:136.746
[100]: test's l2:136.692
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.058522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.085
[3]: test's l2:145.971
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.096
[7]: test's l2:143.655
[8]: test's l2:143.334
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.41
[18]: test's l2:141.305
[19]: test's l2:141.189
[20]: test's l2:140.948
[21]: test's l2:140.851
[22]: test's l2:140.723
[23]: test's l2:140.544
[24]: test's l2:140.393
[25]: test's l2:140.275
[26]: test's l2:140.161
[27]: test's l2:140.108
[28]: test's l2:139.963
[29]: test's l2:139.883
[30]: test's l2:139.685
[31]: test's l2:139.529
[32]: test's l2:139.459
[33]: test's l2:139.466
[34]: test's l2:139.359
[35]: test's l2:139.332
[36]: test's l2:139.259
[37]: test's l2:139.134
[38]: test's l2:139.153
[39]: test's l2:139.175
[40]: test's l2:139.146
[41]: test's l2:139.063
[42]: test's l2:138.928
[43]: test's l2:138.823
[44]: test's l2:138.827
[45]: test's l2:138.785
[46]: test's l2:138.734
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.525
[50]: test's l2:138.499
[51]: test's l2:138.478
[52]: test's l2:138.469
[53]: test's l2:138.364
[54]: test's l2:138.343
[55]: test's l2:138.342
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.088
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.887
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.58
[68]: test's l2:137.55
[69]: test's l2:137.511
[70]: test's l2:137.514
[71]: test's l2:137.474
[72]: test's l2:137.475
[73]: test's l2:137.436
[74]: test's l2:137.388
[75]: test's l2:137.353
[76]: test's l2:137.375
[77]: test's l2:137.326
[78]: test's l2:137.357
[79]: test's l2:137.26
[80]: test's l2:137.195
[81]: test's l2:137.139
[82]: test's l2:137.137
[83]: test's l2:137.101
[84]: test's l2:137.143
[85]: test's l2:137.136
[86]: test's l2:137.086
[87]: test's l2:137.091
[88]: test's l2:137.014
[89]: test's l2:137.035
[90]: test's l2:137.042
[91]: test's l2:136.976
[92]: test's l2:136.921
[93]: test's l2:136.931
[94]: test's l2:136.873
[95]: test's l2:136.827
[96]: test's l2:136.777
[97]: test's l2:136.774
[98]: test's l2:136.725
[99]: test's l2:136.746
[100]: test's l2:136.692
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.059520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.278
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.689
[31]: test's l2:139.533
[32]: test's l2:139.463
[33]: test's l2:139.47
[34]: test's l2:139.363
[35]: test's l2:139.336
[36]: test's l2:139.263
[37]: test's l2:139.137
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.93
[43]: test's l2:138.824
[44]: test's l2:138.78
[45]: test's l2:138.729
[46]: test's l2:138.691
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.524
[50]: test's l2:138.499
[51]: test's l2:138.477
[52]: test's l2:138.468
[53]: test's l2:138.363
[54]: test's l2:138.342
[55]: test's l2:138.341
[56]: test's l2:138.313
[57]: test's l2:138.243
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.993
[62]: test's l2:137.895
[63]: test's l2:137.885
[64]: test's l2:137.814
[65]: test's l2:137.752
[66]: test's l2:137.671
[67]: test's l2:137.578
[68]: test's l2:137.548
[69]: test's l2:137.509
[70]: test's l2:137.512
[71]: test's l2:137.472
[72]: test's l2:137.433
[73]: test's l2:137.434
[74]: test's l2:137.386
[75]: test's l2:137.351
[76]: test's l2:137.373
[77]: test's l2:137.323
[78]: test's l2:137.354
[79]: test's l2:137.257
[80]: test's l2:137.194
[81]: test's l2:137.138
[82]: test's l2:137.135
[83]: test's l2:137.1
[84]: test's l2:137.092
[85]: test's l2:137.134
[86]: test's l2:137.084
[87]: test's l2:137.089
[88]: test's l2:137.012
[89]: test's l2:137.032
[90]: test's l2:137.039
[91]: test's l2:136.973
[92]: test's l2:136.918
[93]: test's l2:136.928
[94]: test's l2:136.87
[95]: test's l2:136.823
[96]: test's l2:136.774
[97]: test's l2:136.77
[98]: test's l2:136.722
[99]: test's l2:136.742
[100]: test's l2:136.688
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.055647 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.308
[10]: test's l2:143.032
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.278
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.689
[31]: test's l2:139.533
[32]: test's l2:139.463
[33]: test's l2:139.47
[34]: test's l2:139.363
[35]: test's l2:139.336
[36]: test's l2:139.263
[37]: test's l2:139.137
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.93
[43]: test's l2:138.824
[44]: test's l2:138.78
[45]: test's l2:138.729
[46]: test's l2:138.691
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.524
[50]: test's l2:138.499
[51]: test's l2:138.477
[52]: test's l2:138.468
[53]: test's l2:138.363
[54]: test's l2:138.342
[55]: test's l2:138.341
[56]: test's l2:138.313
[57]: test's l2:138.243
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.993
[62]: test's l2:137.895
[63]: test's l2:137.885
[64]: test's l2:137.814
[65]: test's l2:137.752
[66]: test's l2:137.671
[67]: test's l2:137.578
[68]: test's l2:137.548
[69]: test's l2:137.509
[70]: test's l2:137.512
[71]: test's l2:137.472
[72]: test's l2:137.433
[73]: test's l2:137.434
[74]: test's l2:137.386
[75]: test's l2:137.351
[76]: test's l2:137.373
[77]: test's l2:137.323
[78]: test's l2:137.354
[79]: test's l2:137.257
[80]: test's l2:137.194
[81]: test's l2:137.138
[82]: test's l2:137.135
[83]: test's l2:137.1
[84]: test's l2:137.092
[85]: test's l2:137.134
[86]: test's l2:137.084
[87]: test's l2:137.089
[88]: test's l2:137.012
[89]: test's l2:137.032
[90]: test's l2:137.039
[91]: test's l2:136.973
[92]: test's l2:136.918
[93]: test's l2:136.928
[94]: test's l2:136.87
[95]: test's l2:136.823
[96]: test's l2:136.774
[97]: test's l2:136.77
[98]: test's l2:136.722
[99]: test's l2:136.742
[100]: test's l2:136.688
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.065752 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.308
[10]: test's l2:143.031
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.278
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.689
[31]: test's l2:139.533
[32]: test's l2:139.464
[33]: test's l2:139.47
[34]: test's l2:139.363
[35]: test's l2:139.336
[36]: test's l2:139.263
[37]: test's l2:139.138
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.931
[43]: test's l2:138.824
[44]: test's l2:138.78
[45]: test's l2:138.729
[46]: test's l2:138.691
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.524
[50]: test's l2:138.499
[51]: test's l2:138.477
[52]: test's l2:138.468
[53]: test's l2:138.362
[54]: test's l2:138.342
[55]: test's l2:138.34
[56]: test's l2:138.312
[57]: test's l2:138.242
[58]: test's l2:138.234
[59]: test's l2:138.149
[60]: test's l2:138.086
[61]: test's l2:137.993
[62]: test's l2:137.894
[63]: test's l2:137.884
[64]: test's l2:137.814
[65]: test's l2:137.752
[66]: test's l2:137.671
[67]: test's l2:137.578
[68]: test's l2:137.548
[69]: test's l2:137.509
[70]: test's l2:137.512
[71]: test's l2:137.472
[72]: test's l2:137.433
[73]: test's l2:137.434
[74]: test's l2:137.386
[75]: test's l2:137.417
[76]: test's l2:137.369
[77]: test's l2:137.334
[78]: test's l2:137.356
[79]: test's l2:137.259
[80]: test's l2:137.196
[81]: test's l2:137.14
[82]: test's l2:137.137
[83]: test's l2:137.102
[84]: test's l2:137.094
[85]: test's l2:137.136
[86]: test's l2:137.086
[87]: test's l2:137.091
[88]: test's l2:137.014
[89]: test's l2:137.034
[90]: test's l2:137.041
[91]: test's l2:136.975
[92]: test's l2:136.919
[93]: test's l2:136.93
[94]: test's l2:136.872
[95]: test's l2:136.825
[96]: test's l2:136.775
[97]: test's l2:136.772
[98]: test's l2:136.723
[99]: test's l2:136.669
[100]: test's l2:136.689
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.052158 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.308
[10]: test's l2:143.031
[11]: test's l2:142.689
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.278
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.689
[31]: test's l2:139.533
[32]: test's l2:139.464
[33]: test's l2:139.47
[34]: test's l2:139.363
[35]: test's l2:139.336
[36]: test's l2:139.263
[37]: test's l2:139.138
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.931
[43]: test's l2:138.824
[44]: test's l2:138.78
[45]: test's l2:138.729
[46]: test's l2:138.691
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.524
[50]: test's l2:138.499
[51]: test's l2:138.477
[52]: test's l2:138.468
[53]: test's l2:138.362
[54]: test's l2:138.342
[55]: test's l2:138.34
[56]: test's l2:138.312
[57]: test's l2:138.242
[58]: test's l2:138.234
[59]: test's l2:138.149
[60]: test's l2:138.086
[61]: test's l2:137.993
[62]: test's l2:137.894
[63]: test's l2:137.884
[64]: test's l2:137.814
[65]: test's l2:137.752
[66]: test's l2:137.671
[67]: test's l2:137.578
[68]: test's l2:137.548
[69]: test's l2:137.509
[70]: test's l2:137.512
[71]: test's l2:137.472
[72]: test's l2:137.433
[73]: test's l2:137.434
[74]: test's l2:137.386
[75]: test's l2:137.417
[76]: test's l2:137.369
[77]: test's l2:137.334
[78]: test's l2:137.356
[79]: test's l2:137.259
[80]: test's l2:137.196
[81]: test's l2:137.14
[82]: test's l2:137.137
[83]: test's l2:137.102
[84]: test's l2:137.094
[85]: test's l2:137.136
[86]: test's l2:137.086
[87]: test's l2:137.091
[88]: test's l2:137.014
[89]: test's l2:137.034
[90]: test's l2:137.041
[91]: test's l2:136.975
[92]: test's l2:136.919
[93]: test's l2:136.93
[94]: test's l2:136.872
[95]: test's l2:136.825
[96]: test's l2:136.775
[97]: test's l2:136.772
[98]: test's l2:136.723
[99]: test's l2:136.669
[100]: test's l2:136.689
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.054171 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.278
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.69
[31]: test's l2:139.534
[32]: test's l2:139.464
[33]: test's l2:139.471
[34]: test's l2:139.363
[35]: test's l2:139.336
[36]: test's l2:139.264
[37]: test's l2:139.138
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.931
[43]: test's l2:138.824
[44]: test's l2:138.78
[45]: test's l2:138.729
[46]: test's l2:138.691
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.524
[50]: test's l2:138.499
[51]: test's l2:138.477
[52]: test's l2:138.373
[53]: test's l2:138.351
[54]: test's l2:138.343
[55]: test's l2:138.341
[56]: test's l2:138.313
[57]: test's l2:138.243
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.581
[68]: test's l2:137.551
[69]: test's l2:137.512
[70]: test's l2:137.514
[71]: test's l2:137.475
[72]: test's l2:137.436
[73]: test's l2:137.437
[74]: test's l2:137.389
[75]: test's l2:137.354
[76]: test's l2:137.375
[77]: test's l2:137.326
[78]: test's l2:137.357
[79]: test's l2:137.261
[80]: test's l2:137.196
[81]: test's l2:137.14
[82]: test's l2:137.138
[83]: test's l2:137.102
[84]: test's l2:137.095
[85]: test's l2:137.045
[86]: test's l2:137.05
[87]: test's l2:137.091
[88]: test's l2:137.013
[89]: test's l2:137.034
[90]: test's l2:137.041
[91]: test's l2:136.975
[92]: test's l2:136.919
[93]: test's l2:136.929
[94]: test's l2:136.871
[95]: test's l2:136.825
[96]: test's l2:136.775
[97]: test's l2:136.772
[98]: test's l2:136.723
[99]: test's l2:136.669
[100]: test's l2:136.689
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.065158 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.278
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.69
[31]: test's l2:139.534
[32]: test's l2:139.464
[33]: test's l2:139.471
[34]: test's l2:139.363
[35]: test's l2:139.336
[36]: test's l2:139.264
[37]: test's l2:139.138
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.931
[43]: test's l2:138.824
[44]: test's l2:138.78
[45]: test's l2:138.729
[46]: test's l2:138.691
[47]: test's l2:138.697
[48]: test's l2:138.565
[49]: test's l2:138.524
[50]: test's l2:138.499
[51]: test's l2:138.477
[52]: test's l2:138.373
[53]: test's l2:138.351
[54]: test's l2:138.343
[55]: test's l2:138.341
[56]: test's l2:138.313
[57]: test's l2:138.243
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.581
[68]: test's l2:137.551
[69]: test's l2:137.512
[70]: test's l2:137.514
[71]: test's l2:137.475
[72]: test's l2:137.436
[73]: test's l2:137.437
[74]: test's l2:137.389
[75]: test's l2:137.354
[76]: test's l2:137.375
[77]: test's l2:137.326
[78]: test's l2:137.357
[79]: test's l2:137.261
[80]: test's l2:137.196
[81]: test's l2:137.14
[82]: test's l2:137.138
[83]: test's l2:137.102
[84]: test's l2:137.095
[85]: test's l2:137.045
[86]: test's l2:137.05
[87]: test's l2:137.091
[88]: test's l2:137.013
[89]: test's l2:137.034
[90]: test's l2:137.041
[91]: test's l2:136.975
[92]: test's l2:136.919
[93]: test's l2:136.929
[94]: test's l2:136.871
[95]: test's l2:136.825
[96]: test's l2:136.775
[97]: test's l2:136.772
[98]: test's l2:136.723
[99]: test's l2:136.669
[100]: test's l2:136.689
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.043199 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.279
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.69
[31]: test's l2:139.534
[32]: test's l2:139.465
[33]: test's l2:139.471
[34]: test's l2:139.364
[35]: test's l2:139.336
[36]: test's l2:139.264
[37]: test's l2:139.138
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.931
[43]: test's l2:138.824
[44]: test's l2:138.829
[45]: test's l2:138.787
[46]: test's l2:138.736
[47]: test's l2:138.698
[48]: test's l2:138.566
[49]: test's l2:138.526
[50]: test's l2:138.501
[51]: test's l2:138.478
[52]: test's l2:138.374
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.991
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.581
[68]: test's l2:137.551
[69]: test's l2:137.512
[70]: test's l2:137.514
[71]: test's l2:137.475
[72]: test's l2:137.475
[73]: test's l2:137.436
[74]: test's l2:137.388
[75]: test's l2:137.353
[76]: test's l2:137.375
[77]: test's l2:137.326
[78]: test's l2:137.357
[79]: test's l2:137.26
[80]: test's l2:137.196
[81]: test's l2:137.14
[82]: test's l2:137.137
[83]: test's l2:137.102
[84]: test's l2:137.094
[85]: test's l2:137.044
[86]: test's l2:137.049
[87]: test's l2:137.09
[88]: test's l2:137.013
[89]: test's l2:137.033
[90]: test's l2:137.04
[91]: test's l2:136.974
[92]: test's l2:136.918
[93]: test's l2:136.928
[94]: test's l2:136.87
[95]: test's l2:136.824
[96]: test's l2:136.774
[97]: test's l2:136.771
[98]: test's l2:136.716
[99]: test's l2:136.721
[100]: test's l2:136.68
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.279
[26]: test's l2:140.164
[27]: test's l2:140.111
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.69
[31]: test's l2:139.534
[32]: test's l2:139.465
[33]: test's l2:139.471
[34]: test's l2:139.364
[35]: test's l2:139.336
[36]: test's l2:139.264
[37]: test's l2:139.138
[38]: test's l2:139.156
[39]: test's l2:139.177
[40]: test's l2:139.148
[41]: test's l2:139.064
[42]: test's l2:138.931
[43]: test's l2:138.824
[44]: test's l2:138.829
[45]: test's l2:138.787
[46]: test's l2:138.736
[47]: test's l2:138.698
[48]: test's l2:138.566
[49]: test's l2:138.526
[50]: test's l2:138.501
[51]: test's l2:138.478
[52]: test's l2:138.374
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.991
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.581
[68]: test's l2:137.551
[69]: test's l2:137.512
[70]: test's l2:137.514
[71]: test's l2:137.475
[72]: test's l2:137.475
[73]: test's l2:137.436
[74]: test's l2:137.388
[75]: test's l2:137.353
[76]: test's l2:137.375
[77]: test's l2:137.326
[78]: test's l2:137.357
[79]: test's l2:137.26
[80]: test's l2:137.196
[81]: test's l2:137.14
[82]: test's l2:137.137
[83]: test's l2:137.102
[84]: test's l2:137.094
[85]: test's l2:137.044
[86]: test's l2:137.049
[87]: test's l2:137.09
[88]: test's l2:137.013
[89]: test's l2:137.033
[90]: test's l2:137.04
[91]: test's l2:136.974
[92]: test's l2:136.918
[93]: test's l2:136.928
[94]: test's l2:136.87
[95]: test's l2:136.824
[96]: test's l2:136.774
[97]: test's l2:136.771
[98]: test's l2:136.716
[99]: test's l2:136.721
[100]: test's l2:136.68
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062597 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.279
[26]: test's l2:140.164
[27]: test's l2:140.112
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.69
[31]: test's l2:139.535
[32]: test's l2:139.465
[33]: test's l2:139.471
[34]: test's l2:139.364
[35]: test's l2:139.336
[36]: test's l2:139.217
[37]: test's l2:139.14
[38]: test's l2:139.158
[39]: test's l2:139.179
[40]: test's l2:139.149
[41]: test's l2:139.066
[42]: test's l2:138.932
[43]: test's l2:138.826
[44]: test's l2:138.83
[45]: test's l2:138.788
[46]: test's l2:138.737
[47]: test's l2:138.7
[48]: test's l2:138.568
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.48
[52]: test's l2:138.375
[53]: test's l2:138.354
[54]: test's l2:138.345
[55]: test's l2:138.317
[56]: test's l2:138.316
[57]: test's l2:138.246
[58]: test's l2:138.237
[59]: test's l2:138.152
[60]: test's l2:138.088
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.91
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.675
[67]: test's l2:137.582
[68]: test's l2:137.552
[69]: test's l2:137.513
[70]: test's l2:137.515
[71]: test's l2:137.475
[72]: test's l2:137.475
[73]: test's l2:137.451
[74]: test's l2:137.431
[75]: test's l2:137.383
[76]: test's l2:137.413
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.3
[80]: test's l2:137.307
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.335
[85]: test's l2:137.26
[86]: test's l2:137.192
[87]: test's l2:137.182
[88]: test's l2:137.116
[89]: test's l2:137.135
[90]: test's l2:137.13
[91]: test's l2:137.124
[92]: test's l2:137.136
[93]: test's l2:137.148
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.057098 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.399
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.655
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.109
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.413
[18]: test's l2:141.308
[19]: test's l2:141.192
[20]: test's l2:140.951
[21]: test's l2:140.854
[22]: test's l2:140.726
[23]: test's l2:140.547
[24]: test's l2:140.396
[25]: test's l2:140.279
[26]: test's l2:140.164
[27]: test's l2:140.112
[28]: test's l2:139.966
[29]: test's l2:139.886
[30]: test's l2:139.69
[31]: test's l2:139.535
[32]: test's l2:139.465
[33]: test's l2:139.471
[34]: test's l2:139.364
[35]: test's l2:139.336
[36]: test's l2:139.217
[37]: test's l2:139.14
[38]: test's l2:139.158
[39]: test's l2:139.179
[40]: test's l2:139.149
[41]: test's l2:139.066
[42]: test's l2:138.932
[43]: test's l2:138.826
[44]: test's l2:138.83
[45]: test's l2:138.788
[46]: test's l2:138.737
[47]: test's l2:138.7
[48]: test's l2:138.568
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.48
[52]: test's l2:138.375
[53]: test's l2:138.354
[54]: test's l2:138.345
[55]: test's l2:138.317
[56]: test's l2:138.316
[57]: test's l2:138.246
[58]: test's l2:138.237
[59]: test's l2:138.152
[60]: test's l2:138.088
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.91
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.675
[67]: test's l2:137.582
[68]: test's l2:137.552
[69]: test's l2:137.513
[70]: test's l2:137.515
[71]: test's l2:137.475
[72]: test's l2:137.475
[73]: test's l2:137.451
[74]: test's l2:137.431
[75]: test's l2:137.383
[76]: test's l2:137.413
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.3
[80]: test's l2:137.307
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.335
[85]: test's l2:137.26
[86]: test's l2:137.192
[87]: test's l2:137.182
[88]: test's l2:137.116
[89]: test's l2:137.135
[90]: test's l2:137.13
[91]: test's l2:137.124
[92]: test's l2:137.136
[93]: test's l2:137.148
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063447 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.311
[19]: test's l2:141.195
[20]: test's l2:140.954
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.55
[24]: test's l2:140.399
[25]: test's l2:140.282
[26]: test's l2:140.167
[27]: test's l2:140.115
[28]: test's l2:139.969
[29]: test's l2:139.889
[30]: test's l2:139.694
[31]: test's l2:139.539
[32]: test's l2:139.469
[33]: test's l2:139.475
[34]: test's l2:139.368
[35]: test's l2:139.34
[36]: test's l2:139.221
[37]: test's l2:139.144
[38]: test's l2:139.162
[39]: test's l2:139.132
[40]: test's l2:139.049
[41]: test's l2:139.068
[42]: test's l2:138.935
[43]: test's l2:138.827
[44]: test's l2:138.783
[45]: test's l2:138.732
[46]: test's l2:138.694
[47]: test's l2:138.7
[48]: test's l2:138.568
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.479
[52]: test's l2:138.375
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.245
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.675
[67]: test's l2:137.644
[68]: test's l2:137.606
[69]: test's l2:137.514
[70]: test's l2:137.515
[71]: test's l2:137.476
[72]: test's l2:137.437
[73]: test's l2:137.389
[74]: test's l2:137.354
[75]: test's l2:137.293
[76]: test's l2:137.188
[77]: test's l2:137.134
[78]: test's l2:137.165
[79]: test's l2:137.186
[80]: test's l2:137.129
[81]: test's l2:137.122
[82]: test's l2:137.119
[83]: test's l2:137.084
[84]: test's l2:137.034
[85]: test's l2:137.039
[86]: test's l2:137.079
[87]: test's l2:137.089
[88]: test's l2:137.031
[89]: test's l2:136.953
[90]: test's l2:136.972
[91]: test's l2:136.978
[92]: test's l2:136.944
[93]: test's l2:136.946
[94]: test's l2:136.893
[95]: test's l2:136.825
[96]: test's l2:136.814
[97]: test's l2:136.766
[98]: test's l2:136.763
[99]: test's l2:136.707
[100]: test's l2:136.725
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.050719 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.311
[19]: test's l2:141.195
[20]: test's l2:140.954
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.55
[24]: test's l2:140.399
[25]: test's l2:140.282
[26]: test's l2:140.167
[27]: test's l2:140.115
[28]: test's l2:139.969
[29]: test's l2:139.889
[30]: test's l2:139.694
[31]: test's l2:139.539
[32]: test's l2:139.469
[33]: test's l2:139.475
[34]: test's l2:139.368
[35]: test's l2:139.34
[36]: test's l2:139.221
[37]: test's l2:139.144
[38]: test's l2:139.162
[39]: test's l2:139.132
[40]: test's l2:139.049
[41]: test's l2:139.068
[42]: test's l2:138.935
[43]: test's l2:138.827
[44]: test's l2:138.783
[45]: test's l2:138.732
[46]: test's l2:138.694
[47]: test's l2:138.7
[48]: test's l2:138.568
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.479
[52]: test's l2:138.375
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.245
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.991
[62]: test's l2:137.98
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.675
[67]: test's l2:137.644
[68]: test's l2:137.606
[69]: test's l2:137.514
[70]: test's l2:137.515
[71]: test's l2:137.476
[72]: test's l2:137.437
[73]: test's l2:137.389
[74]: test's l2:137.354
[75]: test's l2:137.293
[76]: test's l2:137.188
[77]: test's l2:137.134
[78]: test's l2:137.165
[79]: test's l2:137.186
[80]: test's l2:137.129
[81]: test's l2:137.122
[82]: test's l2:137.119
[83]: test's l2:137.084
[84]: test's l2:137.034
[85]: test's l2:137.039
[86]: test's l2:137.079
[87]: test's l2:137.089
[88]: test's l2:137.031
[89]: test's l2:136.953
[90]: test's l2:136.972
[91]: test's l2:136.978
[92]: test's l2:136.944
[93]: test's l2:136.946
[94]: test's l2:136.893
[95]: test's l2:136.825
[96]: test's l2:136.814
[97]: test's l2:136.766
[98]: test's l2:136.763
[99]: test's l2:136.707
[100]: test's l2:136.725
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.058594 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.311
[19]: test's l2:141.195
[20]: test's l2:140.954
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.55
[24]: test's l2:140.399
[25]: test's l2:140.282
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.969
[29]: test's l2:139.889
[30]: test's l2:139.695
[31]: test's l2:139.539
[32]: test's l2:139.47
[33]: test's l2:139.476
[34]: test's l2:139.368
[35]: test's l2:139.34
[36]: test's l2:139.221
[37]: test's l2:139.144
[38]: test's l2:139.162
[39]: test's l2:139.132
[40]: test's l2:139.049
[41]: test's l2:139.068
[42]: test's l2:138.935
[43]: test's l2:138.827
[44]: test's l2:138.783
[45]: test's l2:138.732
[46]: test's l2:138.694
[47]: test's l2:138.7
[48]: test's l2:138.569
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.479
[52]: test's l2:138.375
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.606
[69]: test's l2:137.514
[70]: test's l2:137.515
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.384
[75]: test's l2:137.414
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.3
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.335
[85]: test's l2:137.259
[86]: test's l2:137.192
[87]: test's l2:137.182
[88]: test's l2:137.115
[89]: test's l2:137.135
[90]: test's l2:137.129
[91]: test's l2:137.124
[92]: test's l2:137.135
[93]: test's l2:137.146
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.048571 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.031
[11]: test's l2:142.688
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.311
[19]: test's l2:141.195
[20]: test's l2:140.954
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.55
[24]: test's l2:140.399
[25]: test's l2:140.282
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.969
[29]: test's l2:139.889
[30]: test's l2:139.695
[31]: test's l2:139.539
[32]: test's l2:139.47
[33]: test's l2:139.476
[34]: test's l2:139.368
[35]: test's l2:139.34
[36]: test's l2:139.221
[37]: test's l2:139.144
[38]: test's l2:139.162
[39]: test's l2:139.132
[40]: test's l2:139.049
[41]: test's l2:139.068
[42]: test's l2:138.935
[43]: test's l2:138.827
[44]: test's l2:138.783
[45]: test's l2:138.732
[46]: test's l2:138.694
[47]: test's l2:138.7
[48]: test's l2:138.569
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.479
[52]: test's l2:138.375
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.606
[69]: test's l2:137.514
[70]: test's l2:137.515
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.384
[75]: test's l2:137.414
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.3
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.335
[85]: test's l2:137.259
[86]: test's l2:137.192
[87]: test's l2:137.182
[88]: test's l2:137.115
[89]: test's l2:137.135
[90]: test's l2:137.129
[91]: test's l2:137.124
[92]: test's l2:137.135
[93]: test's l2:137.146
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.044882 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.312
[19]: test's l2:141.195
[20]: test's l2:140.954
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.55
[24]: test's l2:140.4
[25]: test's l2:140.282
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.969
[29]: test's l2:139.89
[30]: test's l2:139.695
[31]: test's l2:139.54
[32]: test's l2:139.47
[33]: test's l2:139.476
[34]: test's l2:139.369
[35]: test's l2:139.341
[36]: test's l2:139.221
[37]: test's l2:139.145
[38]: test's l2:139.162
[39]: test's l2:139.132
[40]: test's l2:139.049
[41]: test's l2:139.068
[42]: test's l2:138.936
[43]: test's l2:138.828
[44]: test's l2:138.784
[45]: test's l2:138.732
[46]: test's l2:138.694
[47]: test's l2:138.7
[48]: test's l2:138.569
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.479
[52]: test's l2:138.375
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.086
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.514
[70]: test's l2:137.515
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.384
[75]: test's l2:137.414
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.3
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.334
[85]: test's l2:137.259
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.129
[91]: test's l2:137.123
[92]: test's l2:137.134
[93]: test's l2:137.145
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049022 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.307
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.312
[19]: test's l2:141.195
[20]: test's l2:140.954
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.55
[24]: test's l2:140.4
[25]: test's l2:140.282
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.969
[29]: test's l2:139.89
[30]: test's l2:139.695
[31]: test's l2:139.54
[32]: test's l2:139.47
[33]: test's l2:139.476
[34]: test's l2:139.369
[35]: test's l2:139.341
[36]: test's l2:139.221
[37]: test's l2:139.145
[38]: test's l2:139.162
[39]: test's l2:139.132
[40]: test's l2:139.049
[41]: test's l2:139.068
[42]: test's l2:138.936
[43]: test's l2:138.828
[44]: test's l2:138.784
[45]: test's l2:138.732
[46]: test's l2:138.694
[47]: test's l2:138.7
[48]: test's l2:138.569
[49]: test's l2:138.528
[50]: test's l2:138.502
[51]: test's l2:138.479
[52]: test's l2:138.375
[53]: test's l2:138.353
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.086
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.514
[70]: test's l2:137.515
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.384
[75]: test's l2:137.414
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.3
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.334
[85]: test's l2:137.259
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.129
[91]: test's l2:137.123
[92]: test's l2:137.134
[93]: test's l2:137.145
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034900 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.312
[19]: test's l2:141.195
[20]: test's l2:140.955
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.551
[24]: test's l2:140.4
[25]: test's l2:140.282
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.97
[29]: test's l2:139.89
[30]: test's l2:139.696
[31]: test's l2:139.54
[32]: test's l2:139.471
[33]: test's l2:139.476
[34]: test's l2:139.369
[35]: test's l2:139.341
[36]: test's l2:139.222
[37]: test's l2:139.145
[38]: test's l2:139.162
[39]: test's l2:139.133
[40]: test's l2:139.05
[41]: test's l2:139.069
[42]: test's l2:138.936
[43]: test's l2:138.828
[44]: test's l2:138.833
[45]: test's l2:138.79
[46]: test's l2:138.739
[47]: test's l2:138.702
[48]: test's l2:138.57
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.345
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.245
[58]: test's l2:138.236
[59]: test's l2:138.152
[60]: test's l2:138.087
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.383
[75]: test's l2:137.414
[76]: test's l2:137.413
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.3
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.334
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.123
[92]: test's l2:137.134
[93]: test's l2:137.078
[94]: test's l2:137.024
[95]: test's l2:136.953
[96]: test's l2:136.96
[97]: test's l2:136.97
[98]: test's l2:136.933
[99]: test's l2:136.95
[100]: test's l2:136.928
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033482 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.312
[19]: test's l2:141.195
[20]: test's l2:140.955
[21]: test's l2:140.857
[22]: test's l2:140.729
[23]: test's l2:140.551
[24]: test's l2:140.4
[25]: test's l2:140.282
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.97
[29]: test's l2:139.89
[30]: test's l2:139.696
[31]: test's l2:139.54
[32]: test's l2:139.471
[33]: test's l2:139.476
[34]: test's l2:139.369
[35]: test's l2:139.341
[36]: test's l2:139.222
[37]: test's l2:139.145
[38]: test's l2:139.162
[39]: test's l2:139.133
[40]: test's l2:139.05
[41]: test's l2:139.069
[42]: test's l2:138.936
[43]: test's l2:138.828
[44]: test's l2:138.833
[45]: test's l2:138.79
[46]: test's l2:138.739
[47]: test's l2:138.702
[48]: test's l2:138.57
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.345
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.245
[58]: test's l2:138.236
[59]: test's l2:138.152
[60]: test's l2:138.087
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.383
[75]: test's l2:137.414
[76]: test's l2:137.413
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.3
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.334
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.123
[92]: test's l2:137.134
[93]: test's l2:137.078
[94]: test's l2:137.024
[95]: test's l2:136.953
[96]: test's l2:136.96
[97]: test's l2:136.97
[98]: test's l2:136.933
[99]: test's l2:136.95
[100]: test's l2:136.928
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.076444 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.312
[19]: test's l2:141.195
[20]: test's l2:140.955
[21]: test's l2:140.857
[22]: test's l2:140.73
[23]: test's l2:140.551
[24]: test's l2:140.4
[25]: test's l2:140.283
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.97
[29]: test's l2:139.89
[30]: test's l2:139.696
[31]: test's l2:139.541
[32]: test's l2:139.471
[33]: test's l2:139.477
[34]: test's l2:139.369
[35]: test's l2:139.341
[36]: test's l2:139.222
[37]: test's l2:139.146
[38]: test's l2:139.162
[39]: test's l2:139.133
[40]: test's l2:139.05
[41]: test's l2:139.069
[42]: test's l2:138.936
[43]: test's l2:138.828
[44]: test's l2:138.833
[45]: test's l2:138.79
[46]: test's l2:138.739
[47]: test's l2:138.702
[48]: test's l2:138.571
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.345
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.245
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.383
[75]: test's l2:137.414
[76]: test's l2:137.413
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.3
[80]: test's l2:137.307
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.333
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.122
[92]: test's l2:137.133
[93]: test's l2:137.077
[94]: test's l2:137.024
[95]: test's l2:136.953
[96]: test's l2:136.96
[97]: test's l2:136.969
[98]: test's l2:136.933
[99]: test's l2:136.949
[100]: test's l2:136.927
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.056702 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.301
[2]: test's l2:147.084
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.333
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.11
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.416
[18]: test's l2:141.312
[19]: test's l2:141.195
[20]: test's l2:140.955
[21]: test's l2:140.857
[22]: test's l2:140.73
[23]: test's l2:140.551
[24]: test's l2:140.4
[25]: test's l2:140.283
[26]: test's l2:140.168
[27]: test's l2:140.115
[28]: test's l2:139.97
[29]: test's l2:139.89
[30]: test's l2:139.696
[31]: test's l2:139.541
[32]: test's l2:139.471
[33]: test's l2:139.477
[34]: test's l2:139.369
[35]: test's l2:139.341
[36]: test's l2:139.222
[37]: test's l2:139.146
[38]: test's l2:139.162
[39]: test's l2:139.133
[40]: test's l2:139.05
[41]: test's l2:139.069
[42]: test's l2:138.936
[43]: test's l2:138.828
[44]: test's l2:138.833
[45]: test's l2:138.79
[46]: test's l2:138.739
[47]: test's l2:138.702
[48]: test's l2:138.571
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.345
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.245
[58]: test's l2:138.236
[59]: test's l2:138.151
[60]: test's l2:138.087
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.816
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.476
[72]: test's l2:137.451
[73]: test's l2:137.431
[74]: test's l2:137.383
[75]: test's l2:137.414
[76]: test's l2:137.413
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.3
[80]: test's l2:137.307
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.333
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.122
[92]: test's l2:137.133
[93]: test's l2:137.077
[94]: test's l2:137.024
[95]: test's l2:136.953
[96]: test's l2:136.96
[97]: test's l2:136.969
[98]: test's l2:136.933
[99]: test's l2:136.949
[100]: test's l2:136.927
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044611 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.948
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.198
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.403
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.118
[28]: test's l2:139.973
[29]: test's l2:139.893
[30]: test's l2:139.7
[31]: test's l2:139.545
[32]: test's l2:139.475
[33]: test's l2:139.481
[34]: test's l2:139.373
[35]: test's l2:139.345
[36]: test's l2:139.225
[37]: test's l2:139.149
[38]: test's l2:139.166
[39]: test's l2:139.136
[40]: test's l2:139.053
[41]: test's l2:139.071
[42]: test's l2:138.939
[43]: test's l2:138.83
[44]: test's l2:138.786
[45]: test's l2:138.735
[46]: test's l2:138.696
[47]: test's l2:138.702
[48]: test's l2:138.571
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.151
[60]: test's l2:138.086
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.477
[72]: test's l2:137.452
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.332
[82]: test's l2:137.328
[83]: test's l2:137.295
[84]: test's l2:137.22
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.123
[92]: test's l2:137.134
[93]: test's l2:137.077
[94]: test's l2:137.024
[95]: test's l2:136.953
[96]: test's l2:136.96
[97]: test's l2:136.969
[98]: test's l2:136.932
[99]: test's l2:136.948
[100]: test's l2:136.969
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.051120 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.802
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.948
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.198
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.403
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.118
[28]: test's l2:139.973
[29]: test's l2:139.893
[30]: test's l2:139.7
[31]: test's l2:139.545
[32]: test's l2:139.475
[33]: test's l2:139.481
[34]: test's l2:139.373
[35]: test's l2:139.345
[36]: test's l2:139.225
[37]: test's l2:139.149
[38]: test's l2:139.166
[39]: test's l2:139.136
[40]: test's l2:139.053
[41]: test's l2:139.071
[42]: test's l2:138.939
[43]: test's l2:138.83
[44]: test's l2:138.786
[45]: test's l2:138.735
[46]: test's l2:138.696
[47]: test's l2:138.702
[48]: test's l2:138.571
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.151
[60]: test's l2:138.086
[61]: test's l2:137.99
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.477
[72]: test's l2:137.452
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.332
[82]: test's l2:137.328
[83]: test's l2:137.295
[84]: test's l2:137.22
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.123
[92]: test's l2:137.134
[93]: test's l2:137.077
[94]: test's l2:137.024
[95]: test's l2:136.953
[96]: test's l2:136.96
[97]: test's l2:136.969
[98]: test's l2:136.932
[99]: test's l2:136.948
[100]: test's l2:136.969
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.056511 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.948
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.199
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.403
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.118
[28]: test's l2:139.973
[29]: test's l2:139.893
[30]: test's l2:139.701
[31]: test's l2:139.546
[32]: test's l2:139.476
[33]: test's l2:139.481
[34]: test's l2:139.374
[35]: test's l2:139.345
[36]: test's l2:139.226
[37]: test's l2:139.15
[38]: test's l2:139.166
[39]: test's l2:139.137
[40]: test's l2:139.054
[41]: test's l2:139.071
[42]: test's l2:138.939
[43]: test's l2:138.83
[44]: test's l2:138.786
[45]: test's l2:138.735
[46]: test's l2:138.696
[47]: test's l2:138.703
[48]: test's l2:138.571
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.989
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.477
[72]: test's l2:137.452
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.332
[82]: test's l2:137.328
[83]: test's l2:137.295
[84]: test's l2:137.22
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.122
[92]: test's l2:137.133
[93]: test's l2:137.077
[94]: test's l2:137.024
[95]: test's l2:136.952
[96]: test's l2:136.959
[97]: test's l2:136.923
[98]: test's l2:136.932
[99]: test's l2:136.947
[100]: test's l2:136.921
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.051197 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.03
[11]: test's l2:142.687
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.948
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.199
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.403
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.118
[28]: test's l2:139.973
[29]: test's l2:139.893
[30]: test's l2:139.701
[31]: test's l2:139.546
[32]: test's l2:139.476
[33]: test's l2:139.481
[34]: test's l2:139.374
[35]: test's l2:139.345
[36]: test's l2:139.226
[37]: test's l2:139.15
[38]: test's l2:139.166
[39]: test's l2:139.137
[40]: test's l2:139.054
[41]: test's l2:139.071
[42]: test's l2:138.939
[43]: test's l2:138.83
[44]: test's l2:138.786
[45]: test's l2:138.735
[46]: test's l2:138.696
[47]: test's l2:138.703
[48]: test's l2:138.571
[49]: test's l2:138.53
[50]: test's l2:138.504
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.989
[62]: test's l2:137.979
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.607
[70]: test's l2:137.568
[71]: test's l2:137.477
[72]: test's l2:137.452
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.395
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.332
[82]: test's l2:137.328
[83]: test's l2:137.295
[84]: test's l2:137.22
[85]: test's l2:137.258
[86]: test's l2:137.191
[87]: test's l2:137.181
[88]: test's l2:137.115
[89]: test's l2:137.134
[90]: test's l2:137.128
[91]: test's l2:137.122
[92]: test's l2:137.133
[93]: test's l2:137.077
[94]: test's l2:137.024
[95]: test's l2:136.952
[96]: test's l2:136.959
[97]: test's l2:136.923
[98]: test's l2:136.932
[99]: test's l2:136.947
[100]: test's l2:136.921
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.029
[11]: test's l2:142.686
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.199
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.403
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.118
[28]: test's l2:139.973
[29]: test's l2:139.893
[30]: test's l2:139.701
[31]: test's l2:139.546
[32]: test's l2:139.476
[33]: test's l2:139.482
[34]: test's l2:139.374
[35]: test's l2:139.345
[36]: test's l2:139.226
[37]: test's l2:139.15
[38]: test's l2:139.167
[39]: test's l2:139.137
[40]: test's l2:139.054
[41]: test's l2:138.922
[42]: test's l2:138.94
[43]: test's l2:138.83
[44]: test's l2:138.786
[45]: test's l2:138.735
[46]: test's l2:138.697
[47]: test's l2:138.703
[48]: test's l2:138.572
[49]: test's l2:138.531
[50]: test's l2:138.505
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.989
[62]: test's l2:137.978
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.579
[70]: test's l2:137.559
[71]: test's l2:137.561
[72]: test's l2:137.523
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.219
[85]: test's l2:137.152
[86]: test's l2:137.142
[87]: test's l2:137.076
[88]: test's l2:137.095
[89]: test's l2:137.089
[90]: test's l2:137.113
[91]: test's l2:137.108
[92]: test's l2:137.039
[93]: test's l2:136.986
[94]: test's l2:136.948
[95]: test's l2:136.955
[96]: test's l2:136.964
[97]: test's l2:136.975
[98]: test's l2:136.914
[99]: test's l2:136.93
[100]: test's l2:136.903
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.057181 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.029
[11]: test's l2:142.686
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.199
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.403
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.118
[28]: test's l2:139.973
[29]: test's l2:139.893
[30]: test's l2:139.701
[31]: test's l2:139.546
[32]: test's l2:139.476
[33]: test's l2:139.482
[34]: test's l2:139.374
[35]: test's l2:139.345
[36]: test's l2:139.226
[37]: test's l2:139.15
[38]: test's l2:139.167
[39]: test's l2:139.137
[40]: test's l2:139.054
[41]: test's l2:138.922
[42]: test's l2:138.94
[43]: test's l2:138.83
[44]: test's l2:138.786
[45]: test's l2:138.735
[46]: test's l2:138.697
[47]: test's l2:138.703
[48]: test's l2:138.572
[49]: test's l2:138.531
[50]: test's l2:138.505
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.989
[62]: test's l2:137.978
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.674
[67]: test's l2:137.644
[68]: test's l2:137.605
[69]: test's l2:137.579
[70]: test's l2:137.559
[71]: test's l2:137.561
[72]: test's l2:137.523
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.219
[85]: test's l2:137.152
[86]: test's l2:137.142
[87]: test's l2:137.076
[88]: test's l2:137.095
[89]: test's l2:137.089
[90]: test's l2:137.113
[91]: test's l2:137.108
[92]: test's l2:137.039
[93]: test's l2:136.986
[94]: test's l2:136.948
[95]: test's l2:136.955
[96]: test's l2:136.964
[97]: test's l2:136.975
[98]: test's l2:136.914
[99]: test's l2:136.93
[100]: test's l2:136.903
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059271 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.029
[11]: test's l2:142.686
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.199
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.404
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.119
[28]: test's l2:139.973
[29]: test's l2:139.894
[30]: test's l2:139.702
[31]: test's l2:139.547
[32]: test's l2:139.477
[33]: test's l2:139.482
[34]: test's l2:139.374
[35]: test's l2:139.346
[36]: test's l2:139.226
[37]: test's l2:139.151
[38]: test's l2:139.167
[39]: test's l2:139.137
[40]: test's l2:139.054
[41]: test's l2:138.923
[42]: test's l2:138.814
[43]: test's l2:138.831
[44]: test's l2:138.787
[45]: test's l2:138.736
[46]: test's l2:138.697
[47]: test's l2:138.703
[48]: test's l2:138.572
[49]: test's l2:138.531
[50]: test's l2:138.505
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.989
[62]: test's l2:137.978
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.675
[67]: test's l2:137.645
[68]: test's l2:137.606
[69]: test's l2:137.579
[70]: test's l2:137.559
[71]: test's l2:137.561
[72]: test's l2:137.523
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.219
[85]: test's l2:137.152
[86]: test's l2:137.142
[87]: test's l2:137.076
[88]: test's l2:137.095
[89]: test's l2:137.089
[90]: test's l2:137.113
[91]: test's l2:137.107
[92]: test's l2:137.039
[93]: test's l2:136.986
[94]: test's l2:136.948
[95]: test's l2:136.955
[96]: test's l2:136.964
[97]: test's l2:136.975
[98]: test's l2:136.914
[99]: test's l2:136.929
[100]: test's l2:136.903
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035984 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.095
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.306
[10]: test's l2:143.029
[11]: test's l2:142.686
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.315
[19]: test's l2:141.199
[20]: test's l2:140.958
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.404
[25]: test's l2:140.286
[26]: test's l2:140.172
[27]: test's l2:140.119
[28]: test's l2:139.973
[29]: test's l2:139.894
[30]: test's l2:139.702
[31]: test's l2:139.547
[32]: test's l2:139.477
[33]: test's l2:139.482
[34]: test's l2:139.374
[35]: test's l2:139.346
[36]: test's l2:139.226
[37]: test's l2:139.151
[38]: test's l2:139.167
[39]: test's l2:139.137
[40]: test's l2:139.054
[41]: test's l2:138.923
[42]: test's l2:138.814
[43]: test's l2:138.831
[44]: test's l2:138.787
[45]: test's l2:138.736
[46]: test's l2:138.697
[47]: test's l2:138.703
[48]: test's l2:138.572
[49]: test's l2:138.531
[50]: test's l2:138.505
[51]: test's l2:138.481
[52]: test's l2:138.376
[53]: test's l2:138.354
[54]: test's l2:138.344
[55]: test's l2:138.315
[56]: test's l2:138.314
[57]: test's l2:138.244
[58]: test's l2:138.235
[59]: test's l2:138.15
[60]: test's l2:138.086
[61]: test's l2:137.989
[62]: test's l2:137.978
[63]: test's l2:137.909
[64]: test's l2:137.817
[65]: test's l2:137.755
[66]: test's l2:137.675
[67]: test's l2:137.645
[68]: test's l2:137.606
[69]: test's l2:137.579
[70]: test's l2:137.559
[71]: test's l2:137.561
[72]: test's l2:137.523
[73]: test's l2:137.432
[74]: test's l2:137.384
[75]: test's l2:137.415
[76]: test's l2:137.376
[77]: test's l2:137.375
[78]: test's l2:137.394
[79]: test's l2:137.301
[80]: test's l2:137.308
[81]: test's l2:137.331
[82]: test's l2:137.328
[83]: test's l2:137.294
[84]: test's l2:137.219
[85]: test's l2:137.152
[86]: test's l2:137.142
[87]: test's l2:137.076
[88]: test's l2:137.095
[89]: test's l2:137.089
[90]: test's l2:137.113
[91]: test's l2:137.107
[92]: test's l2:137.039
[93]: test's l2:136.986
[94]: test's l2:136.948
[95]: test's l2:136.955
[96]: test's l2:136.964
[97]: test's l2:136.975
[98]: test's l2:136.914
[99]: test's l2:136.929
[100]: test's l2:136.903
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062640 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.094
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.305
[10]: test's l2:143.029
[11]: test's l2:142.686
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.316
[19]: test's l2:141.199
[20]: test's l2:140.959
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.404
[25]: test's l2:140.287
[26]: test's l2:140.172
[27]: test's l2:140.119
[28]: test's l2:139.974
[29]: test's l2:139.894
[30]: test's l2:139.702
[31]: test's l2:139.547
[32]: test's l2:139.478
[33]: test's l2:139.483
[34]: test's l2:139.375
[35]: test's l2:139.346
[36]: test's l2:139.227
[37]: test's l2:139.151
[38]: test's l2:139.167
[39]: test's l2:139.137
[40]: test's l2:139.055
[41]: test's l2:138.923
[42]: test's l2:138.814
[43]: test's l2:138.831
[44]: test's l2:138.836
[45]: test's l2:138.793
[46]: test's l2:138.742
[47]: test's l2:138.705
[48]: test's l2:138.574
[49]: test's l2:138.533
[50]: test's l2:138.507
[51]: test's l2:138.483
[52]: test's l2:138.377
[53]: test's l2:138.355
[54]: test's l2:138.346
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.263
[58]: test's l2:138.254
[59]: test's l2:138.169
[60]: test's l2:138.105
[61]: test's l2:138.007
[62]: test's l2:137.996
[63]: test's l2:137.927
[64]: test's l2:137.835
[65]: test's l2:137.773
[66]: test's l2:137.693
[67]: test's l2:137.666
[68]: test's l2:137.647
[69]: test's l2:137.648
[70]: test's l2:137.608
[71]: test's l2:137.578
[72]: test's l2:137.539
[73]: test's l2:137.449
[74]: test's l2:137.401
[75]: test's l2:137.432
[76]: test's l2:137.43
[77]: test's l2:137.449
[78]: test's l2:137.411
[79]: test's l2:137.317
[80]: test's l2:137.324
[81]: test's l2:137.347
[82]: test's l2:137.344
[83]: test's l2:137.31
[84]: test's l2:137.235
[85]: test's l2:137.168
[86]: test's l2:137.158
[87]: test's l2:137.181
[88]: test's l2:137.097
[89]: test's l2:137.12
[90]: test's l2:137.114
[91]: test's l2:137.109
[92]: test's l2:137.054
[93]: test's l2:137.016
[94]: test's l2:137.027
[95]: test's l2:136.97
[96]: test's l2:136.977
[97]: test's l2:136.907
[98]: test's l2:136.921
[99]: test's l2:136.931
[100]: test's l2:136.906
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.050324 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.3
[2]: test's l2:147.083
[3]: test's l2:145.97
[4]: test's l2:145.398
[5]: test's l2:144.801
[6]: test's l2:144.094
[7]: test's l2:143.654
[8]: test's l2:143.332
[9]: test's l2:143.305
[10]: test's l2:143.029
[11]: test's l2:142.686
[12]: test's l2:142.381
[13]: test's l2:142.111
[14]: test's l2:141.947
[15]: test's l2:141.777
[16]: test's l2:141.73
[17]: test's l2:141.42
[18]: test's l2:141.316
[19]: test's l2:141.199
[20]: test's l2:140.959
[21]: test's l2:140.861
[22]: test's l2:140.733
[23]: test's l2:140.554
[24]: test's l2:140.404
[25]: test's l2:140.287
[26]: test's l2:140.172
[27]: test's l2:140.119
[28]: test's l2:139.974
[29]: test's l2:139.894
[30]: test's l2:139.702
[31]: test's l2:139.547
[32]: test's l2:139.478
[33]: test's l2:139.483
[34]: test's l2:139.375
[35]: test's l2:139.346
[36]: test's l2:139.227
[37]: test's l2:139.151
[38]: test's l2:139.167
[39]: test's l2:139.137
[40]: test's l2:139.055
[41]: test's l2:138.923
[42]: test's l2:138.814
[43]: test's l2:138.831
[44]: test's l2:138.836
[45]: test's l2:138.793
[46]: test's l2:138.742
[47]: test's l2:138.705
[48]: test's l2:138.574
[49]: test's l2:138.533
[50]: test's l2:138.507
[51]: test's l2:138.483
[52]: test's l2:138.377
[53]: test's l2:138.355
[54]: test's l2:138.346
[55]: test's l2:138.316
[56]: test's l2:138.315
[57]: test's l2:138.263
[58]: test's l2:138.254
[59]: test's l2:138.169
[60]: test's l2:138.105
[61]: test's l2:138.007
[62]: test's l2:137.996
[63]: test's l2:137.927
[64]: test's l2:137.835
[65]: test's l2:137.773
[66]: test's l2:137.693
[67]: test's l2:137.666
[68]: test's l2:137.647
[69]: test's l2:137.648
[70]: test's l2:137.608
[71]: test's l2:137.578
[72]: test's l2:137.539
[73]: test's l2:137.449
[74]: test's l2:137.401
[75]: test's l2:137.432
[76]: test's l2:137.43
[77]: test's l2:137.449
[78]: test's l2:137.411
[79]: test's l2:137.317
[80]: test's l2:137.324
[81]: test's l2:137.347
[82]: test's l2:137.344
[83]: test's l2:137.31
[84]: test's l2:137.235
[85]: test's l2:137.168
[86]: test's l2:137.158
[87]: test's l2:137.181
[88]: test's l2:137.097
[89]: test's l2:137.12
[90]: test's l2:137.114
[91]: test's l2:137.109
[92]: test's l2:137.054
[93]: test's l2:137.016
[94]: test's l2:137.027
[95]: test's l2:136.97
[96]: test's l2:136.977
[97]: test's l2:136.907
[98]: test's l2:136.921
[99]: test's l2:136.931
[100]: test's l2:136.906
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.038403 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.367
[2]: test's l2:147.156
[3]: test's l2:146.037
[4]: test's l2:145.455
[5]: test's l2:144.854
[6]: test's l2:144.154
[7]: test's l2:143.719
[8]: test's l2:143.403
[9]: test's l2:143.373
[10]: test's l2:143.096
[11]: test's l2:142.765
[12]: test's l2:142.46
[13]: test's l2:142.19
[14]: test's l2:142.026
[15]: test's l2:141.857
[16]: test's l2:141.808
[17]: test's l2:141.702
[18]: test's l2:141.395
[19]: test's l2:141.278
[20]: test's l2:141.172
[21]: test's l2:141.047
[22]: test's l2:140.813
[23]: test's l2:140.637
[24]: test's l2:140.486
[25]: test's l2:140.372
[26]: test's l2:140.256
[27]: test's l2:140.201
[28]: test's l2:140.055
[29]: test's l2:139.974
[30]: test's l2:139.783
[31]: test's l2:139.787
[32]: test's l2:139.634
[33]: test's l2:139.564
[34]: test's l2:139.458
[35]: test's l2:139.43
[36]: test's l2:139.309
[37]: test's l2:139.232
[38]: test's l2:139.247
[39]: test's l2:139.233
[40]: test's l2:139.196
[41]: test's l2:139.085
[42]: test's l2:138.954
[43]: test's l2:138.97
[44]: test's l2:138.931
[45]: test's l2:138.887
[46]: test's l2:138.836
[47]: test's l2:138.811
[48]: test's l2:138.781
[49]: test's l2:138.647
[50]: test's l2:138.606
[51]: test's l2:138.613
[52]: test's l2:138.506
[53]: test's l2:138.484
[54]: test's l2:138.522
[55]: test's l2:138.512
[56]: test's l2:138.51
[57]: test's l2:138.457
[58]: test's l2:138.448
[59]: test's l2:138.364
[60]: test's l2:138.301
[61]: test's l2:138.266
[62]: test's l2:138.166
[63]: test's l2:138.161
[64]: test's l2:138.144
[65]: test's l2:138.058
[66]: test's l2:137.99
[67]: test's l2:137.92
[68]: test's l2:137.889
[69]: test's l2:137.851
[70]: test's l2:137.76
[71]: test's l2:137.711
[72]: test's l2:137.677
[73]: test's l2:137.698
[74]: test's l2:137.631
[75]: test's l2:137.655
[76]: test's l2:137.555
[77]: test's l2:137.508
[78]: test's l2:137.539
[79]: test's l2:137.467
[80]: test's l2:137.457
[81]: test's l2:137.456
[82]: test's l2:137.449
[83]: test's l2:137.411
[84]: test's l2:137.392
[85]: test's l2:137.321
[86]: test's l2:137.331
[87]: test's l2:137.355
[88]: test's l2:137.365
[89]: test's l2:137.301
[90]: test's l2:137.338
[91]: test's l2:137.291
[92]: test's l2:137.286
[93]: test's l2:137.195
[94]: test's l2:137.138
[95]: test's l2:137.134
[96]: test's l2:137.099
[97]: test's l2:137.055
[98]: test's l2:136.998
[99]: test's l2:136.982
[100]: test's l2:136.99
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.059785 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.367
[2]: test's l2:147.156
[3]: test's l2:146.037
[4]: test's l2:145.455
[5]: test's l2:144.854
[6]: test's l2:144.154
[7]: test's l2:143.719
[8]: test's l2:143.403
[9]: test's l2:143.373
[10]: test's l2:143.096
[11]: test's l2:142.765
[12]: test's l2:142.46
[13]: test's l2:142.19
[14]: test's l2:142.026
[15]: test's l2:141.857
[16]: test's l2:141.808
[17]: test's l2:141.702
[18]: test's l2:141.395
[19]: test's l2:141.278
[20]: test's l2:141.172
[21]: test's l2:141.047
[22]: test's l2:140.813
[23]: test's l2:140.637
[24]: test's l2:140.486
[25]: test's l2:140.372
[26]: test's l2:140.256
[27]: test's l2:140.201
[28]: test's l2:140.055
[29]: test's l2:139.974
[30]: test's l2:139.783
[31]: test's l2:139.787
[32]: test's l2:139.634
[33]: test's l2:139.564
[34]: test's l2:139.458
[35]: test's l2:139.43
[36]: test's l2:139.309
[37]: test's l2:139.232
[38]: test's l2:139.247
[39]: test's l2:139.233
[40]: test's l2:139.196
[41]: test's l2:139.085
[42]: test's l2:138.954
[43]: test's l2:138.97
[44]: test's l2:138.931
[45]: test's l2:138.887
[46]: test's l2:138.836
[47]: test's l2:138.811
[48]: test's l2:138.781
[49]: test's l2:138.647
[50]: test's l2:138.606
[51]: test's l2:138.613
[52]: test's l2:138.506
[53]: test's l2:138.484
[54]: test's l2:138.522
[55]: test's l2:138.512
[56]: test's l2:138.51
[57]: test's l2:138.457
[58]: test's l2:138.448
[59]: test's l2:138.364
[60]: test's l2:138.301
[61]: test's l2:138.266
[62]: test's l2:138.166
[63]: test's l2:138.161
[64]: test's l2:138.144
[65]: test's l2:138.058
[66]: test's l2:137.99
[67]: test's l2:137.92
[68]: test's l2:137.889
[69]: test's l2:137.851
[70]: test's l2:137.76
[71]: test's l2:137.711
[72]: test's l2:137.677
[73]: test's l2:137.698
[74]: test's l2:137.631
[75]: test's l2:137.655
[76]: test's l2:137.555
[77]: test's l2:137.508
[78]: test's l2:137.539
[79]: test's l2:137.467
[80]: test's l2:137.457
[81]: test's l2:137.456
[82]: test's l2:137.449
[83]: test's l2:137.411
[84]: test's l2:137.392
[85]: test's l2:137.321
[86]: test's l2:137.331
[87]: test's l2:137.355
[88]: test's l2:137.365
[89]: test's l2:137.301
[90]: test's l2:137.338
[91]: test's l2:137.291
[92]: test's l2:137.286
[93]: test's l2:137.195
[94]: test's l2:137.138
[95]: test's l2:137.134
[96]: test's l2:137.099
[97]: test's l2:137.055
[98]: test's l2:136.998
[99]: test's l2:136.982
[100]: test's l2:136.99
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.044343 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.367
[2]: test's l2:147.156
[3]: test's l2:146.037
[4]: test's l2:145.455
[5]: test's l2:144.854
[6]: test's l2:144.154
[7]: test's l2:143.719
[8]: test's l2:143.403
[9]: test's l2:143.373
[10]: test's l2:143.096
[11]: test's l2:142.765
[12]: test's l2:142.46
[13]: test's l2:142.19
[14]: test's l2:142.026
[15]: test's l2:141.856
[16]: test's l2:141.807
[17]: test's l2:141.702
[18]: test's l2:141.395
[19]: test's l2:141.278
[20]: test's l2:141.172
[21]: test's l2:141.047
[22]: test's l2:140.813
[23]: test's l2:140.637
[24]: test's l2:140.486
[25]: test's l2:140.373
[26]: test's l2:140.256
[27]: test's l2:140.201
[28]: test's l2:140.055
[29]: test's l2:139.975
[30]: test's l2:139.783
[31]: test's l2:139.787
[32]: test's l2:139.635
[33]: test's l2:139.565
[34]: test's l2:139.459
[35]: test's l2:139.43
[36]: test's l2:139.309
[37]: test's l2:139.232
[38]: test's l2:139.248
[39]: test's l2:139.233
[40]: test's l2:139.196
[41]: test's l2:139.086
[42]: test's l2:138.955
[43]: test's l2:138.97
[44]: test's l2:138.932
[45]: test's l2:138.888
[46]: test's l2:138.836
[47]: test's l2:138.811
[48]: test's l2:138.781
[49]: test's l2:138.648
[50]: test's l2:138.606
[51]: test's l2:138.614
[52]: test's l2:138.506
[53]: test's l2:138.484
[54]: test's l2:138.522
[55]: test's l2:138.512
[56]: test's l2:138.51
[57]: test's l2:138.457
[58]: test's l2:138.448
[59]: test's l2:138.364
[60]: test's l2:138.3
[61]: test's l2:138.266
[62]: test's l2:138.165
[63]: test's l2:138.161
[64]: test's l2:138.144
[65]: test's l2:138.058
[66]: test's l2:137.99
[67]: test's l2:137.92
[68]: test's l2:137.889
[69]: test's l2:137.851
[70]: test's l2:137.76
[71]: test's l2:137.711
[72]: test's l2:137.677
[73]: test's l2:137.698
[74]: test's l2:137.631
[75]: test's l2:137.655
[76]: test's l2:137.555
[77]: test's l2:137.508
[78]: test's l2:137.539
[79]: test's l2:137.467
[80]: test's l2:137.457
[81]: test's l2:137.456
[82]: test's l2:137.449
[83]: test's l2:137.411
[84]: test's l2:137.392
[85]: test's l2:137.321
[86]: test's l2:137.331
[87]: test's l2:137.355
[88]: test's l2:137.364
[89]: test's l2:137.301
[90]: test's l2:137.271
[91]: test's l2:137.308
[92]: test's l2:137.304
[93]: test's l2:137.255
[94]: test's l2:137.166
[95]: test's l2:137.162
[96]: test's l2:137.128
[97]: test's l2:137.072
[98]: test's l2:137.056
[99]: test's l2:137.013
[100]: test's l2:136.955
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062881 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.367
[2]: test's l2:147.156
[3]: test's l2:146.037
[4]: test's l2:145.455
[5]: test's l2:144.854
[6]: test's l2:144.154
[7]: test's l2:143.719
[8]: test's l2:143.403
[9]: test's l2:143.373
[10]: test's l2:143.096
[11]: test's l2:142.765
[12]: test's l2:142.46
[13]: test's l2:142.19
[14]: test's l2:142.026
[15]: test's l2:141.856
[16]: test's l2:141.807
[17]: test's l2:141.702
[18]: test's l2:141.395
[19]: test's l2:141.278
[20]: test's l2:141.172
[21]: test's l2:141.047
[22]: test's l2:140.813
[23]: test's l2:140.637
[24]: test's l2:140.486
[25]: test's l2:140.373
[26]: test's l2:140.256
[27]: test's l2:140.201
[28]: test's l2:140.055
[29]: test's l2:139.975
[30]: test's l2:139.783
[31]: test's l2:139.787
[32]: test's l2:139.635
[33]: test's l2:139.565
[34]: test's l2:139.459
[35]: test's l2:139.43
[36]: test's l2:139.309
[37]: test's l2:139.232
[38]: test's l2:139.248
[39]: test's l2:139.233
[40]: test's l2:139.196
[41]: test's l2:139.086
[42]: test's l2:138.955
[43]: test's l2:138.97
[44]: test's l2:138.932
[45]: test's l2:138.888
[46]: test's l2:138.836
[47]: test's l2:138.811
[48]: test's l2:138.781
[49]: test's l2:138.648
[50]: test's l2:138.606
[51]: test's l2:138.614
[52]: test's l2:138.506
[53]: test's l2:138.484
[54]: test's l2:138.522
[55]: test's l2:138.512
[56]: test's l2:138.51
[57]: test's l2:138.457
[58]: test's l2:138.448
[59]: test's l2:138.364
[60]: test's l2:138.3
[61]: test's l2:138.266
[62]: test's l2:138.165
[63]: test's l2:138.161
[64]: test's l2:138.144
[65]: test's l2:138.058
[66]: test's l2:137.99
[67]: test's l2:137.92
[68]: test's l2:137.889
[69]: test's l2:137.851
[70]: test's l2:137.76
[71]: test's l2:137.711
[72]: test's l2:137.677
[73]: test's l2:137.698
[74]: test's l2:137.631
[75]: test's l2:137.655
[76]: test's l2:137.555
[77]: test's l2:137.508
[78]: test's l2:137.539
[79]: test's l2:137.467
[80]: test's l2:137.457
[81]: test's l2:137.456
[82]: test's l2:137.449
[83]: test's l2:137.411
[84]: test's l2:137.392
[85]: test's l2:137.321
[86]: test's l2:137.331
[87]: test's l2:137.355
[88]: test's l2:137.364
[89]: test's l2:137.301
[90]: test's l2:137.271
[91]: test's l2:137.308
[92]: test's l2:137.304
[93]: test's l2:137.255
[94]: test's l2:137.166
[95]: test's l2:137.162
[96]: test's l2:137.128
[97]: test's l2:137.072
[98]: test's l2:137.056
[99]: test's l2:137.013
[100]: test's l2:136.955
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.058157 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.366
[2]: test's l2:147.156
[3]: test's l2:146.037
[4]: test's l2:145.455
[5]: test's l2:144.854
[6]: test's l2:144.154
[7]: test's l2:143.719
[8]: test's l2:143.403
[9]: test's l2:143.373
[10]: test's l2:143.096
[11]: test's l2:142.765
[12]: test's l2:142.46
[13]: test's l2:142.19
[14]: test's l2:142.026
[15]: test's l2:141.856
[16]: test's l2:141.807
[17]: test's l2:141.702
[18]: test's l2:141.396
[19]: test's l2:141.278
[20]: test's l2:141.172
[21]: test's l2:141.048
[22]: test's l2:140.813
[23]: test's l2:140.637
[24]: test's l2:140.487
[25]: test's l2:140.373
[26]: test's l2:140.256
[27]: test's l2:140.202
[28]: test's l2:140.056
[29]: test's l2:139.975
[30]: test's l2:139.784
[31]: test's l2:139.788
[32]: test's l2:139.635
[33]: test's l2:139.565
[34]: test's l2:139.459
[35]: test's l2:139.43
[36]: test's l2:139.309
[37]: test's l2:139.232
[38]: test's l2:139.248
[39]: test's l2:139.234
[40]: test's l2:139.196
[41]: test's l2:139.086
[42]: test's l2:138.955
[43]: test's l2:138.971
[44]: test's l2:138.932
[45]: test's l2:138.888
[46]: test's l2:138.837
[47]: test's l2:138.811
[48]: test's l2:138.781
[49]: test's l2:138.648
[50]: test's l2:138.606
[51]: test's l2:138.614
[52]: test's l2:138.506
[53]: test's l2:138.484
[54]: test's l2:138.522
[55]: test's l2:138.511
[56]: test's l2:138.509
[57]: test's l2:138.457
[58]: test's l2:138.448
[59]: test's l2:138.364
[60]: test's l2:138.3
[61]: test's l2:138.266
[62]: test's l2:138.165
[63]: test's l2:138.161
[64]: test's l2:138.143
[65]: test's l2:138.076
[66]: test's l2:137.99
[67]: test's l2:137.919
[68]: test's l2:137.889
[69]: test's l2:137.85
[70]: test's l2:137.76
[71]: test's l2:137.711
[72]: test's l2:137.677
[73]: test's l2:137.698
[74]: test's l2:137.631
[75]: test's l2:137.655
[76]: test's l2:137.555
[77]: test's l2:137.508
[78]: test's l2:137.539
[79]: test's l2:137.467
[80]: test's l2:137.457
[81]: test's l2:137.45
[82]: test's l2:137.449
[83]: test's l2:137.411
[84]: test's l2:137.392
[85]: test's l2:137.321
[86]: test's l2:137.33
[87]: test's l2:137.355
[88]: test's l2:137.364
[89]: test's l2:137.301
[90]: test's l2:137.271
[91]: test's l2:137.307
[92]: test's l2:137.303
[93]: test's l2:137.255
[94]: test's l2:137.166
[95]: test's l2:137.162
[96]: test's l2:137.127
[97]: test's l2:137.071
[98]: test's l2:137.055
[99]: test's l2:137.012
[100]: test's l2:136.955
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.057903 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.366
[2]: test's l2:147.156
[3]: test's l2:146.037
[4]: test's l2:145.455
[5]: test's l2:144.854
[6]: test's l2:144.154
[7]: test's l2:143.719
[8]: test's l2:143.403
[9]: test's l2:143.373
[10]: test's l2:143.096
[11]: test's l2:142.765
[12]: test's l2:142.46
[13]: test's l2:142.19
[14]: test's l2:142.026
[15]: test's l2:141.856
[16]: test's l2:141.807
[17]: test's l2:141.702
[18]: test's l2:141.396
[19]: test's l2:141.278
[20]: test's l2:141.172
[21]: test's l2:141.048
[22]: test's l2:140.813
[23]: test's l2:140.637
[24]: test's l2:140.487
[25]: test's l2:140.373
[26]: test's l2:140.256
[27]: test's l2:140.202
[28]: test's l2:140.056
[29]: test's l2:139.975
[30]: test's l2:139.784
[31]: test's l2:139.788
[32]: test's l2:139.635
[33]: test's l2:139.565
[34]: test's l2:139.459
[35]: test's l2:139.43
[36]: test's l2:139.309
[37]: test's l2:139.232
[38]: test's l2:139.248
[39]: test's l2:139.234
[40]: test's l2:139.196
[41]: test's l2:139.086
[42]: test's l2:138.955
[43]: test's l2:138.971
[44]: test's l2:138.932
[45]: test's l2:138.888
[46]: test's l2:138.837
[47]: test's l2:138.811
[48]: test's l2:138.781
[49]: test's l2:138.648
[50]: test's l2:138.606
[51]: test's l2:138.614
[52]: test's l2:138.506
[53]: test's l2:138.484
[54]: test's l2:138.522
[55]: test's l2:138.511
[56]: test's l2:138.509
[57]: test's l2:138.457
[58]: test's l2:138.448
[59]: test's l2:138.364
[60]: test's l2:138.3
[61]: test's l2:138.266
[62]: test's l2:138.165
[63]: test's l2:138.161
[64]: test's l2:138.143
[65]: test's l2:138.076
[66]: test's l2:137.99
[67]: test's l2:137.919
[68]: test's l2:137.889
[69]: test's l2:137.85
[70]: test's l2:137.76
[71]: test's l2:137.711
[72]: test's l2:137.677
[73]: test's l2:137.698
[74]: test's l2:137.631
[75]: test's l2:137.655
[76]: test's l2:137.555
[77]: test's l2:137.508
[78]: test's l2:137.539
[79]: test's l2:137.467
[80]: test's l2:137.457
[81]: test's l2:137.45
[82]: test's l2:137.449
[83]: test's l2:137.411
[84]: test's l2:137.392
[85]: test's l2:137.321
[86]: test's l2:137.33
[87]: test's l2:137.355
[88]: test's l2:137.364
[89]: test's l2:137.301
[90]: test's l2:137.271
[91]: test's l2:137.307
[92]: test's l2:137.303
[93]: test's l2:137.255
[94]: test's l2:137.166
[95]: test's l2:137.162
[96]: test's l2:137.127
[97]: test's l2:137.071
[98]: test's l2:137.055
[99]: test's l2:137.012
[100]: test's l2:136.955
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.057516 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
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[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.051094 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
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[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.063687 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
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[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Unknown parameter: Depth
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.061104 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 38450
[LightGBM] [Info] Number of data points in the train set: 47099, number of used features: 451
[LightGBM] [Info] Start training from score 29.119387
[1]: test's l2:148.366
[2]: test's l2:147.156
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cat("Model ", which.min(perf), " is lowest loss: ", min(perf), sep = "")
Model 13 is lowest loss: 136.6688
print(grid_search[which.min(perf), ])
Algorithms score is around 0.3 and computational time is:
lgbtaketime <- lstoptime - lstarttime
cat("The algorithms takes ", lgbtaketime, "seconds")
The algorithms takes 2.702765 seconds
catstartTime <- Sys.time()
fit_params <- list(l2_leaf_reg = 0.001,
depth=6,
learning_rate = 0.1,
iterations = 100,
random_seed = 233)
pool = catboost.load_pool(as.matrix(train_set.num_X), label = as.integer(train_set[,1]))
model <- catboost.train(pool, params = fit_params)
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96: learn: 11.5547748 total: 3.72s remaining: 115ms
97: learn: 11.5497704 total: 3.75s remaining: 76.5ms
98: learn: 11.5480024 total: 3.79s remaining: 38.2ms
99: learn: 11.5460042 total: 3.82s remaining: 0us
catstopTime <- Sys.time()
cattakenTime <- catstopTime - catstartTime
calculate the prediction:
#get the prediction
catprediction <- catboost.predict(model,
pool,
prediction_type = 'RawFormulaVal')
calculate the program score:
#round the prediction
catprediction <- round(catprediction,0)
catTable <- table(train_set$Str_h_texture,catprediction)
catTablerow <- rownames(catTable)
catTablecol <- colnames(catTable)
catscore <- sumElementinTable(catTable,catTablerow,catTablecol)/sum(catTable)
cat('The algorithm takes' ,cattakenTime , 'seconds')
The algorithm takes 4.908873 seconds
cat('The algorithm scores' ,catscore)
The algorithm scores 0.02403448
nbstarttime <- Sys.time()
nbClassifier <- naiveBayes(as.factor(Str_h_texture) ~ .,data = train_set,laplace=2)
nbTestPrediction <- predict(nbClassifier,test_set,type = "class")
nbTableTest <- table(nbTestPrediction,test_set$Str_h_texture)
nbTestTablerow <- rownames(nbTableTest)
nbTestTablecol <- colnames(nbTableTest)
nbTestTablescore<- sumElementinTable(nbTableTest,nbTestTablerow,nbTestTablecol)/sum(nbTableTest)
nbendtime <- Sys.time()
nbTrainPrediction <- predict(nbClassifier,train_set,type = "class")
cat('NaiveBayes takes',nbtakentime,'seconds')
NaiveBayes takes 4.305502 seconds
cat('NaiveBayes score',nbTrainTablescore)
NaiveBayes score 355
fnbstartTime <- Sys.time()
dist <- fnb.detect_distribution(train_set.num_X)
gauss <- fnb.gaussian(train_set.num_X[,dist$gaussian], as.factor(train_set$Str_h_texture),sparse = TRUE,check = FALSE)
pred <- predict(gauss, train_set.num_X[,dist$gaussian])
fnbendTime <- Sys.time()
error <- mean(as.factor(train_set$Str_h_texture)!=pred)
print(error)
[1] 0.9383002
fnbtakentime <- fnbendTime - fnbstartTime
cat("fastNaiveBayes takes ", round(fnbtakentime,6), "seconds")
fastNaiveBayes takes 27.94227 seconds
Data preprocessing
train_set.norm <- train_set
maxStr_h_texture <- max(train_set.norm$Str_h_texture)
minStr_h_texture <- min(train_set.norm$Str_h_texture)
train_set.norm$Str_h_texture <- normalize(train_set.norm$Str_h_texture)
train_set.norm.X <- train_set.norm[,-1]
test_set.norm <- test_set
maxteStr_h_texture <- max(test_set.norm$Str_h_texture)
minteStr_h_texture <- min(test_set.norm$Str_h_texture)
test_set.norm$Str_h_texture <- normalize(test_set.norm$Str_h_texture)
test_set.norm.X <- test_set.norm[,-1]
mlpstarttime <- Sys.time()
model <- mlp(train_set.norm.X, train_set.norm$Str_h_texture, size=5, learnFuncParams=c(0.1),
maxit=50, inputsTest=test_set.norm.X, targetsTest=test_set.norm$Str_h_texture)
summary(model)
SNNS network definition file V1.4-3D
generated at Thu May 14 15:32:26 2020
network name : RSNNS_untitled
source files :
no. of units : 606
no. of connections : 3005
no. of unit types : 0
no. of site types : 0
learning function : Std_Backpropagation
update function : Topological_Order
unit default section :
act | bias | st | subnet | layer | act func | out func
---------|----------|----|--------|-------|--------------|-------------
0.00000 | 0.00000 | i | 0 | 1 | Act_Logistic | Out_Identity
---------|----------|----|--------|-------|--------------|-------------
unit definition section :
no. | typeName | unitName | act | bias | st | position | act func | out func | sites
----|----------|------------------------|----------|----------|----|-------------|--------------|----------|-------
1 | | Input_Str_samp_no | 0.00000 | -0.27258 | i | 1, 0, 0 | Act_Identity | |
2 | | Input_Str_labr_no | 0.00000 | 0.06946 | i | 2, 0, 0 | Act_Identity | |
3 | | Input_Str_X1.40E.02 | 0.00000 | 0.23725 | i | 3, 0, 0 | Act_Identity | |
4 | | Input_Str_X1.40E.04 | 0.00000 | 0.06173 | i | 4, 0, 0 | Act_Identity | |
5 | | Input_Str_X1.80E.03 | 0.00000 | -0.10419 | i | 5, 0, 0 | Act_Identity | |
6 | | Input_Str_X10_BC | 0.00000 | -0.18690 | i | 6, 0, 0 | Act_Identity | |
7 | | Input_Str_X10A_NR | 0.00000 | -0.24656 | i | 7, 0, 0 | Act_Identity | |
8 | | Input_Str_X10A1 | 0.00000 | -0.27116 | i | 8, 0, 0 | Act_Identity | |
9 | | Input_Str_X10B | 0.00000 | 0.24816 | i | 9, 0, 0 | Act_Identity | |
10 | | Input_Str_X10B_NR | 0.00000 | 0.06979 | i | 10, 0, 0 | Act_Identity | |
11 | | Input_Str_X10B1 | 0.00000 | -0.03419 | i | 11, 0, 0 | Act_Identity | |
12 | | Input_Str_X10B3 | 0.00000 | 0.28084 | i | 12, 0, 0 | Act_Identity | |
13 | | Input_Str_X10D1 | 0.00000 | 0.28388 | i | 13, 0, 0 | Act_Identity | |
14 | | Input_Str_X11A1 | 0.00000 | 0.11003 | i | 14, 0, 0 | Act_Identity | |
15 | | Input_Str_X12_HCL_CU | 0.00000 | -0.09536 | i | 15, 0, 0 | Act_Identity | |
16 | | Input_Str_X12_HCL_FE | 0.00000 | 0.05696 | i | 16, 0, 0 | Act_Identity | |
17 | | Input_Str_X12_HCL_MN | 0.00000 | 0.29094 | i | 17, 0, 0 | Act_Identity | |
18 | | Input_Str_X12_HCL_ZN | 0.00000 | -0.03434 | i | 18, 0, 0 | Act_Identity | |
19 | | Input_Str_X12_HF_CU | 0.00000 | 0.28922 | i | 19, 0, 0 | Act_Identity | |
20 | | Input_Str_X12_HF_FE | 0.00000 | -0.00433 | i | 20, 0, 0 | Act_Identity | |
21 | | Input_Str_X12_HF_MN | 0.00000 | -0.07207 | i | 21, 0, 0 | Act_Identity | |
22 | | Input_Str_X12_HF_ZN | 0.00000 | -0.28903 | i | 22, 0, 0 | Act_Identity | |
23 | | Input_Str_X12_NR_CU | 0.00000 | 0.17272 | i | 23, 0, 0 | Act_Identity | |
24 | | Input_Str_X12_NR_FE | 0.00000 | -0.08365 | i | 24, 0, 0 | Act_Identity | |
25 | | Input_Str_X12_NR_MN | 0.00000 | 0.28085 | i | 25, 0, 0 | Act_Identity | |
26 | | Input_Str_X12_NR_ZN | 0.00000 | 0.24954 | i | 26, 0, 0 | Act_Identity | |
27 | | Input_Str_X12_XRF_CU | 0.00000 | 0.19127 | i | 27, 0, 0 | Act_Identity | |
28 | | Input_Str_X12_XRF_FE | 0.00000 | 0.24166 | i | 28, 0, 0 | Act_Identity | |
29 | | Input_Str_X12_XRF_MN | 0.00000 | 0.21554 | i | 29, 0, 0 | Act_Identity | |
30 | | Input_Str_X12_XRF_ZN | 0.00000 | 0.15703 | i | 30, 0, 0 | Act_Identity | |
31 | | Input_Str_X12A1_CD | 0.00000 | -0.01010 | i | 31, 0, 0 | Act_Identity | |
32 | | Input_Str_X12A1_CO | 0.00000 | -0.16547 | i | 32, 0, 0 | Act_Identity | |
33 | | Input_Str_X12A1_Cu | 0.00000 | 0.05173 | i | 33, 0, 0 | Act_Identity | |
34 | | Input_Str_X12A1_CU | 0.00000 | -0.21992 | i | 34, 0, 0 | Act_Identity | |
35 | | Input_Str_X12A1_Fe | 0.00000 | -0.07050 | i | 35, 0, 0 | Act_Identity | |
36 | | Input_Str_X12A1_FE | 0.00000 | -0.25373 | i | 36, 0, 0 | Act_Identity | |
37 | | Input_Str_X12A1_Mn | 0.00000 | 0.10477 | i | 37, 0, 0 | Act_Identity | |
38 | | Input_Str_X12A1_MN | 0.00000 | -0.22350 | i | 38, 0, 0 | Act_Identity | |
39 | | Input_Str_X12A1_PB | 0.00000 | 0.23410 | i | 39, 0, 0 | Act_Identity | |
40 | | Input_Str_X12A1_Zn | 0.00000 | 0.28892 | i | 40, 0, 0 | Act_Identity | |
41 | | Input_Str_X12A1_ZN | 0.00000 | 0.09875 | i | 41, 0, 0 | Act_Identity | |
42 | | Input_Str_X12B1_CU | 0.00000 | -0.13303 | i | 42, 0, 0 | Act_Identity | |
43 | | Input_Str_X12B1_ZN | 0.00000 | -0.01564 | i | 43, 0, 0 | Act_Identity | |
44 | | Input_Str_X12B2_CD | 0.00000 | -0.29526 | i | 44, 0, 0 | Act_Identity | |
45 | | Input_Str_X12B2_CU | 0.00000 | 0.22828 | i | 45, 0, 0 | Act_Identity | |
46 | | Input_Str_X12B2_PB | 0.00000 | -0.29962 | i | 46, 0, 0 | Act_Identity | |
47 | | Input_Str_X12B2_ZN | 0.00000 | 0.13066 | i | 47, 0, 0 | Act_Identity | |
48 | | Input_Str_X12C1 | 0.00000 | 0.13117 | i | 48, 0, 0 | Act_Identity | |
49 | | Input_Str_X12C2 | 0.00000 | -0.17412 | i | 49, 0, 0 | Act_Identity | |
50 | | Input_Str_X13_C_FE | 0.00000 | -0.18161 | i | 50, 0, 0 | Act_Identity | |
51 | | Input_Str_X13_NR_AL | 0.00000 | 0.03981 | i | 51, 0, 0 | Act_Identity | |
52 | | Input_Str_X13_NR_FE | 0.00000 | 0.00260 | i | 52, 0, 0 | Act_Identity | |
53 | | Input_Str_X13_NR_MN | 0.00000 | 0.28979 | i | 53, 0, 0 | Act_Identity | |
54 | | Input_Str_X13A1_AL | 0.00000 | 0.18651 | i | 54, 0, 0 | Act_Identity | |
55 | | Input_Str_X13A1_FE | 0.00000 | -0.25075 | i | 55, 0, 0 | Act_Identity | |
56 | | Input_Str_X13A1_MN | 0.00000 | 0.03059 | i | 56, 0, 0 | Act_Identity | |
57 | | Input_Str_X13A1_SI | 0.00000 | -0.21869 | i | 57, 0, 0 | Act_Identity | |
58 | | Input_Str_X13B1_AL | 0.00000 | -0.25236 | i | 58, 0, 0 | Act_Identity | |
59 | | Input_Str_X13B1_FE | 0.00000 | 0.08336 | i | 59, 0, 0 | Act_Identity | |
60 | | Input_Str_X13C_C_FE | 0.00000 | -0.28898 | i | 60, 0, 0 | Act_Identity | |
61 | | Input_Str_X13C1_AL | 0.00000 | -0.26924 | i | 61, 0, 0 | Act_Identity | |
62 | | Input_Str_X13C1_FE | 0.00000 | 0.15872 | i | 62, 0, 0 | Act_Identity | |
63 | | Input_Str_X13C1_FE203 | 0.00000 | 0.24305 | i | 63, 0, 0 | Act_Identity | |
64 | | Input_Str_X13C1_MN | 0.00000 | -0.17605 | i | 64, 0, 0 | Act_Identity | |
65 | | Input_Str_X13C1_SI | 0.00000 | 0.28677 | i | 65, 0, 0 | Act_Identity | |
66 | | Input_Str_X14_NR_S | 0.00000 | -0.17459 | i | 66, 0, 0 | Act_Identity | |
67 | | Input_Str_X140 | 0.00000 | -0.26045 | i | 67, 0, 0 | Act_Identity | |
68 | | Input_Str_X14B1 | 0.00000 | -0.00156 | i | 68, 0, 0 | Act_Identity | |
69 | | Input_Str_X14C1 | 0.00000 | 0.08435 | i | 69, 0, 0 | Act_Identity | |
70 | | Input_Str_X14D1_C | 0.00000 | 0.22726 | i | 70, 0, 0 | Act_Identity | |
71 | | Input_Str_X14D2_BC | 0.00000 | -0.04861 | i | 71, 0, 0 | Act_Identity | |
72 | | Input_Str_X14F1 | 0.00000 | 0.24815 | i | 72, 0, 0 | Act_Identity | |
73 | | Input_Str_X14H1_CA | 0.00000 | 0.04056 | i | 73, 0, 0 | Act_Identity | |
74 | | Input_Str_X14H1_K | 0.00000 | -0.00980 | i | 74, 0, 0 | Act_Identity | |
75 | | Input_Str_X14H1_MG | 0.00000 | 0.16589 | i | 75, 0, 0 | Act_Identity | |
76 | | Input_Str_X14H1_NA | 0.00000 | -0.27958 | i | 76, 0, 0 | Act_Identity | |
77 | | Input_Str_X15_BASES | 0.00000 | 0.02071 | i | 77, 0, 0 | Act_Identity | |
78 | | Input_Str_X15_HSK_CEC | 0.00000 | -0.25161 | i | 78, 0, 0 | Act_Identity | |
79 | | Input_Str_X15_NR | 0.00000 | -0.01070 | i | 79, 0, 0 | Act_Identity | |
80 | | Input_Str_X15_NR_AL | 0.00000 | 0.22250 | i | 80, 0, 0 | Act_Identity | |
81 | | Input_Str_X15_NR_BSa | 0.00000 | -0.18861 | i | 81, 0, 0 | Act_Identity | |
82 | | Input_Str_X15_NR_BSP | 0.00000 | 0.24917 | i | 82, 0, 0 | Act_Identity | |
83 | | Input_Str_X15_NR_CA | 0.00000 | 0.25714 | i | 83, 0, 0 | Act_Identity | |
84 | | Input_Str_X15_NR_CEC | 0.00000 | 0.21954 | i | 84, 0, 0 | Act_Identity | |
85 | | Input_Str_X15_NR_CMR | 0.00000 | -0.08657 | i | 85, 0, 0 | Act_Identity | |
86 | | Input_Str_X15_NR_ESP | 0.00000 | -0.00947 | i | 86, 0, 0 | Act_Identity | |
87 | | Input_Str_X15_NR_H | 0.00000 | -0.09881 | i | 87, 0, 0 | Act_Identity | |
88 | | Input_Str_X15_NR_K | 0.00000 | -0.24798 | i | 88, 0, 0 | Act_Identity | |
89 | | Input_Str_X15_NR_MG | 0.00000 | -0.11995 | i | 89, 0, 0 | Act_Identity | |
90 | | Input_Str_X15_NR_MN | 0.00000 | 0.07460 | i | 90, 0, 0 | Act_Identity | |
91 | | Input_Str_X15_NR_NA | 0.00000 | 0.21977 | i | 91, 0, 0 | Act_Identity | |
92 | | Input_Str_X15A1_CA | 0.00000 | -0.23548 | i | 92, 0, 0 | Act_Identity | |
93 | | Input_Str_X15A1_CEC | 0.00000 | -0.16904 | i | 93, 0, 0 | Act_Identity | |
94 | | Input_Str_X15A1_K | 0.00000 | 0.23147 | i | 94, 0, 0 | Act_Identity | |
95 | | Input_Str_X15A1_MG | 0.00000 | -0.08637 | i | 95, 0, 0 | Act_Identity | |
96 | | Input_Str_X15A1_MN | 0.00000 | 0.11919 | i | 96, 0, 0 | Act_Identity | |
97 | | Input_Str_X15A1_NA | 0.00000 | -0.14328 | i | 97, 0, 0 | Act_Identity | |
98 | | Input_Str_X15A2_CA | 0.00000 | 0.15249 | i | 98, 0, 0 | Act_Identity | |
99 | | Input_Str_X15A2_CEC | 0.00000 | 0.16314 | i | 99, 0, 0 | Act_Identity | |
100 | | Input_Str_X15A2_K | 0.00000 | -0.29305 | i | 100, 0, 0 | Act_Identity | |
101 | | Input_Str_X15A2_MG | 0.00000 | 0.02862 | i | 101, 0, 0 | Act_Identity | |
102 | | Input_Str_X15A2_NA | 0.00000 | -0.11867 | i | 102, 0, 0 | Act_Identity | |
103 | | Input_Str_X15A3_NA | 0.00000 | 0.25770 | i | 103, 0, 0 | Act_Identity | |
104 | | Input_Str_X15B1_CA | 0.00000 | 0.10375 | i | 104, 0, 0 | Act_Identity | |
105 | | Input_Str_X15B1_CEC | 0.00000 | -0.06241 | i | 105, 0, 0 | Act_Identity | |
106 | | Input_Str_X15B1_K | 0.00000 | -0.21384 | i | 106, 0, 0 | Act_Identity | |
107 | | Input_Str_X15B1_MG | 0.00000 | -0.00269 | i | 107, 0, 0 | Act_Identity | |
108 | | Input_Str_X15B1_NA | 0.00000 | 0.14918 | i | 108, 0, 0 | Act_Identity | |
109 | | Input_Str_X15B2_CA | 0.00000 | 0.28288 | i | 109, 0, 0 | Act_Identity | |
110 | | Input_Str_X15B2_CEC | 0.00000 | -0.13351 | i | 110, 0, 0 | Act_Identity | |
111 | | Input_Str_X15B2_K | 0.00000 | 0.11066 | i | 111, 0, 0 | Act_Identity | |
112 | | Input_Str_X15B2_MG | 0.00000 | 0.01357 | i | 112, 0, 0 | Act_Identity | |
113 | | Input_Str_X15B2_NA | 0.00000 | -0.15645 | i | 113, 0, 0 | Act_Identity | |
114 | | Input_Str_X15C1_CA | 0.00000 | 0.04274 | i | 114, 0, 0 | Act_Identity | |
115 | | Input_Str_X15C1_CEC | 0.00000 | -0.20298 | i | 115, 0, 0 | Act_Identity | |
116 | | Input_Str_X15C1_K | 0.00000 | -0.16222 | i | 116, 0, 0 | Act_Identity | |
117 | | Input_Str_X15C1_MG | 0.00000 | 0.23145 | i | 117, 0, 0 | Act_Identity | |
118 | | Input_Str_X15C1_NA | 0.00000 | -0.05436 | i | 118, 0, 0 | Act_Identity | |
119 | | Input_Str_X15C1mod_CA | 0.00000 | -0.12146 | i | 119, 0, 0 | Act_Identity | |
120 | | Input_Str_X15C1mod_K | 0.00000 | 0.15511 | i | 120, 0, 0 | Act_Identity | |
121 | | Input_Str_X15C1mod_MG | 0.00000 | 0.24041 | i | 121, 0, 0 | Act_Identity | |
122 | | Input_Str_X15C1mod_NA | 0.00000 | -0.09911 | i | 122, 0, 0 | Act_Identity | |
123 | | Input_Str_X15C1modCEC | 0.00000 | -0.01750 | i | 123, 0, 0 | Act_Identity | |
124 | | Input_Str_X15D1_AL | 0.00000 | -0.06458 | i | 124, 0, 0 | Act_Identity | |
125 | | Input_Str_X15D1_CA | 0.00000 | -0.18594 | i | 125, 0, 0 | Act_Identity | |
126 | | Input_Str_X15D1_CEC | 0.00000 | 0.11188 | i | 126, 0, 0 | Act_Identity | |
127 | | Input_Str_X15D1_K | 0.00000 | -0.09905 | i | 127, 0, 0 | Act_Identity | |
128 | | Input_Str_X15D1_MG | 0.00000 | 0.21690 | i | 128, 0, 0 | Act_Identity | |
129 | | Input_Str_X15D1_NA | 0.00000 | 0.01234 | i | 129, 0, 0 | Act_Identity | |
130 | | Input_Str_X15D2_CA | 0.00000 | 0.29113 | i | 130, 0, 0 | Act_Identity | |
131 | | Input_Str_X15D2_CEC | 0.00000 | 0.09137 | i | 131, 0, 0 | Act_Identity | |
132 | | Input_Str_X15D2_K | 0.00000 | -0.28766 | i | 132, 0, 0 | Act_Identity | |
133 | | Input_Str_X15D2_MG | 0.00000 | 0.00844 | i | 133, 0, 0 | Act_Identity | |
134 | | Input_Str_X15D2_NA | 0.00000 | -0.03936 | i | 134, 0, 0 | Act_Identity | |
135 | | Input_Str_X15D3_CA | 0.00000 | 0.16221 | i | 135, 0, 0 | Act_Identity | |
136 | | Input_Str_X15D3_CEC | 0.00000 | -0.01487 | i | 136, 0, 0 | Act_Identity | |
137 | | Input_Str_X15D3_K | 0.00000 | -0.16312 | i | 137, 0, 0 | Act_Identity | |
138 | | Input_Str_X15D3_MG | 0.00000 | 0.22327 | i | 138, 0, 0 | Act_Identity | |
139 | | Input_Str_X15D3_NA | 0.00000 | -0.11933 | i | 139, 0, 0 | Act_Identity | |
140 | | Input_Str_X15E1_AL | 0.00000 | 0.02806 | i | 140, 0, 0 | Act_Identity | |
141 | | Input_Str_X15E1_CA | 0.00000 | 0.08162 | i | 141, 0, 0 | Act_Identity | |
142 | | Input_Str_X15E1_CEC | 0.00000 | -0.13127 | i | 142, 0, 0 | Act_Identity | |
143 | | Input_Str_X15E1_H | 0.00000 | 0.12627 | i | 143, 0, 0 | Act_Identity | |
144 | | Input_Str_X15E1_K | 0.00000 | 0.20116 | i | 144, 0, 0 | Act_Identity | |
145 | | Input_Str_X15E1_MG | 0.00000 | -0.13104 | i | 145, 0, 0 | Act_Identity | |
146 | | Input_Str_X15E1_MN | 0.00000 | -0.09538 | i | 146, 0, 0 | Act_Identity | |
147 | | Input_Str_X15E1_NA | 0.00000 | -0.05283 | i | 147, 0, 0 | Act_Identity | |
148 | | Input_Str_X15E1mod_AL | 0.00000 | 0.15236 | i | 148, 0, 0 | Act_Identity | |
149 | | Input_Str_X15E1mod_CA | 0.00000 | 0.23263 | i | 149, 0, 0 | Act_Identity | |
150 | | Input_Str_X15E1mod_K | 0.00000 | 0.28673 | i | 150, 0, 0 | Act_Identity | |
151 | | Input_Str_X15E1mod_MG | 0.00000 | 0.29932 | i | 151, 0, 0 | Act_Identity | |
152 | | Input_Str_X15E1mod_MN | 0.00000 | -0.08221 | i | 152, 0, 0 | Act_Identity | |
153 | | Input_Str_X15E1mod_NA | 0.00000 | 0.23471 | i | 153, 0, 0 | Act_Identity | |
154 | | Input_Str_X15E2_CA | 0.00000 | 0.23419 | i | 154, 0, 0 | Act_Identity | |
155 | | Input_Str_X15E2_K | 0.00000 | 0.23305 | i | 155, 0, 0 | Act_Identity | |
156 | | Input_Str_X15E2_MG | 0.00000 | -0.26598 | i | 156, 0, 0 | Act_Identity | |
157 | | Input_Str_X15E2_NA | 0.00000 | 0.28838 | i | 157, 0, 0 | Act_Identity | |
158 | | Input_Str_X15E2mod_AL | 0.00000 | -0.06615 | i | 158, 0, 0 | Act_Identity | |
159 | | Input_Str_X15E2mod_CA | 0.00000 | -0.20410 | i | 159, 0, 0 | Act_Identity | |
160 | | Input_Str_X15E2mod_K | 0.00000 | -0.25408 | i | 160, 0, 0 | Act_Identity | |
161 | | Input_Str_X15E2mod_MG | 0.00000 | -0.26174 | i | 161, 0, 0 | Act_Identity | |
162 | | Input_Str_X15E2mod_MN | 0.00000 | 0.14709 | i | 162, 0, 0 | Act_Identity | |
163 | | Input_Str_X15E2mod_NA | 0.00000 | 0.22070 | i | 163, 0, 0 | Act_Identity | |
164 | | Input_Str_X15F1_CA | 0.00000 | -0.09290 | i | 164, 0, 0 | Act_Identity | |
165 | | Input_Str_X15F1_CEC | 0.00000 | -0.12831 | i | 165, 0, 0 | Act_Identity | |
166 | | Input_Str_X15F1_K | 0.00000 | -0.04783 | i | 166, 0, 0 | Act_Identity | |
167 | | Input_Str_X15F1_MG | 0.00000 | 0.11063 | i | 167, 0, 0 | Act_Identity | |
168 | | Input_Str_X15F1_NA | 0.00000 | -0.05910 | i | 168, 0, 0 | Act_Identity | |
169 | | Input_Str_X15F2 | 0.00000 | 0.17137 | i | 169, 0, 0 | Act_Identity | |
170 | | Input_Str_X15F2_AL | 0.00000 | -0.18194 | i | 170, 0, 0 | Act_Identity | |
171 | | Input_Str_X15F3 | 0.00000 | -0.02972 | i | 171, 0, 0 | Act_Identity | |
172 | | Input_Str_X15F4 | 0.00000 | -0.27373 | i | 172, 0, 0 | Act_Identity | |
173 | | Input_Str_X15G_C | 0.00000 | -0.16739 | i | 173, 0, 0 | Act_Identity | |
174 | | Input_Str_X15G_C_AL1 | 0.00000 | 0.02380 | i | 174, 0, 0 | Act_Identity | |
175 | | Input_Str_X15G_C_AL2 | 0.00000 | 0.15656 | i | 175, 0, 0 | Act_Identity | |
176 | | Input_Str_X15G_C_H1 | 0.00000 | 0.00338 | i | 176, 0, 0 | Act_Identity | |
177 | | Input_Str_X15G_D | 0.00000 | -0.10182 | i | 177, 0, 0 | Act_Identity | |
178 | | Input_Str_X15G_H | 0.00000 | 0.10664 | i | 178, 0, 0 | Act_Identity | |
179 | | Input_Str_X15G1 | 0.00000 | 0.05755 | i | 179, 0, 0 | Act_Identity | |
180 | | Input_Str_X15G1_AL | 0.00000 | 0.15477 | i | 180, 0, 0 | Act_Identity | |
181 | | Input_Str_X15G1_H | 0.00000 | -0.17503 | i | 181, 0, 0 | Act_Identity | |
182 | | Input_Str_X15I2 | 0.00000 | -0.11098 | i | 182, 0, 0 | Act_Identity | |
183 | | Input_Str_X15I3 | 0.00000 | -0.21505 | i | 183, 0, 0 | Act_Identity | |
184 | | Input_Str_X15I4 | 0.00000 | 0.19470 | i | 184, 0, 0 | Act_Identity | |
185 | | Input_Str_X15J_BASES | 0.00000 | 0.28933 | i | 185, 0, 0 | Act_Identity | |
186 | | Input_Str_X15J_C | 0.00000 | 0.22366 | i | 186, 0, 0 | Act_Identity | |
187 | | Input_Str_X15J_H | 0.00000 | -0.09023 | i | 187, 0, 0 | Act_Identity | |
188 | | Input_Str_X15J1 | 0.00000 | 0.03928 | i | 188, 0, 0 | Act_Identity | |
189 | | Input_Str_X15J2_MCLW | 0.00000 | 0.12399 | i | 189, 0, 0 | Act_Identity | |
190 | | Input_Str_X15K1 | 0.00000 | -0.18760 | i | 190, 0, 0 | Act_Identity | |
191 | | Input_Str_X15L1 | 0.00000 | -0.18182 | i | 191, 0, 0 | Act_Identity | |
192 | | Input_Str_X15L1_a | 0.00000 | 0.11540 | i | 192, 0, 0 | Act_Identity | |
193 | | Input_Str_X15M1_CMR | 0.00000 | -0.13689 | i | 193, 0, 0 | Act_Identity | |
194 | | Input_Str_X15M1_K.Mg | 0.00000 | 0.00844 | i | 194, 0, 0 | Act_Identity | |
195 | | Input_Str_X15M1AlECEC | 0.00000 | -0.09809 | i | 195, 0, 0 | Act_Identity | |
196 | | Input_Str_X15M1CaCEC | 0.00000 | 0.21286 | i | 196, 0, 0 | Act_Identity | |
197 | | Input_Str_X15M1CaECEC | 0.00000 | 0.17888 | i | 197, 0, 0 | Act_Identity | |
198 | | Input_Str_X15M1KCEC | 0.00000 | -0.18177 | i | 198, 0, 0 | Act_Identity | |
199 | | Input_Str_X15M1KECEC | 0.00000 | 0.10506 | i | 199, 0, 0 | Act_Identity | |
200 | | Input_Str_X15M1MgCEC | 0.00000 | 0.25306 | i | 200, 0, 0 | Act_Identity | |
201 | | Input_Str_X15M1MgECEC | 0.00000 | 0.25435 | i | 201, 0, 0 | Act_Identity | |
202 | | Input_Str_X15N1 | 0.00000 | -0.13708 | i | 202, 0, 0 | Act_Identity | |
203 | | Input_Str_X15N1_a | 0.00000 | -0.22411 | i | 203, 0, 0 | Act_Identity | |
204 | | Input_Str_X15N1_b | 0.00000 | 0.03913 | i | 204, 0, 0 | Act_Identity | |
205 | | Input_Str_X15O1 | 0.00000 | -0.22446 | i | 205, 0, 0 | Act_Identity | |
206 | | Input_Str_X17A_HF. | 0.00000 | 0.14517 | i | 206, 0, 0 | Act_Identity | |
207 | | Input_Str_X17A_NR | 0.00000 | 0.19779 | i | 207, 0, 0 | Act_Identity | |
208 | | Input_Str_X17A1 | 0.00000 | -0.01464 | i | 208, 0, 0 | Act_Identity | |
209 | | Input_Str_X17A3_CA | 0.00000 | 0.24797 | i | 209, 0, 0 | Act_Identity | |
210 | | Input_Str_X17A3_MG | 0.00000 | 0.15194 | i | 210, 0, 0 | Act_Identity | |
211 | | Input_Str_X17A3_NA | 0.00000 | -0.01425 | i | 211, 0, 0 | Act_Identity | |
212 | | Input_Str_X17A3_S | 0.00000 | 0.24215 | i | 212, 0, 0 | Act_Identity | |
213 | | Input_Str_X17D1_CR | 0.00000 | 0.23330 | i | 213, 0, 0 | Act_Identity | |
214 | | Input_Str_X17D1_CU | 0.00000 | -0.18390 | i | 214, 0, 0 | Act_Identity | |
215 | | Input_Str_X17D1_FE | 0.00000 | -0.01097 | i | 215, 0, 0 | Act_Identity | |
216 | | Input_Str_X17D1_MN | 0.00000 | 0.24501 | i | 216, 0, 0 | Act_Identity | |
217 | | Input_Str_X17D1_NI | 0.00000 | 0.08205 | i | 217, 0, 0 | Act_Identity | |
218 | | Input_Str_X17D1_PB | 0.00000 | 0.00750 | i | 218, 0, 0 | Act_Identity | |
219 | | Input_Str_X17D1_ZN | 0.00000 | -0.13770 | i | 219, 0, 0 | Act_Identity | |
220 | | Input_Str_X18_NR | 0.00000 | -0.08164 | i | 220, 0, 0 | Act_Identity | |
221 | | Input_Str_X18_NR_K | 0.00000 | 0.13702 | i | 221, 0, 0 | Act_Identity | |
222 | | Input_Str_X18A1 | 0.00000 | -0.04826 | i | 222, 0, 0 | Act_Identity | |
223 | | Input_Str_X18A1_NR | 0.00000 | -0.17387 | i | 223, 0, 0 | Act_Identity | |
224 | | Input_Str_X18A1mod | 0.00000 | -0.16182 | i | 224, 0, 0 | Act_Identity | |
225 | | Input_Str_X18B1 | 0.00000 | -0.12729 | i | 225, 0, 0 | Act_Identity | |
226 | | Input_Str_X18B2 | 0.00000 | -0.03864 | i | 226, 0, 0 | Act_Identity | |
227 | | Input_Str_X18F1_Al | 0.00000 | 0.07971 | i | 227, 0, 0 | Act_Identity | |
228 | | Input_Str_X18F1_AL | 0.00000 | -0.22650 | i | 228, 0, 0 | Act_Identity | |
229 | | Input_Str_X18F1_As | 0.00000 | 0.25112 | i | 229, 0, 0 | Act_Identity | |
230 | | Input_Str_X18F1_AS | 0.00000 | 0.13628 | i | 230, 0, 0 | Act_Identity | |
231 | | Input_Str_X18F1_B | 0.00000 | 0.24618 | i | 231, 0, 0 | Act_Identity | |
232 | | Input_Str_X18F1_Ca | 0.00000 | 0.17449 | i | 232, 0, 0 | Act_Identity | |
233 | | Input_Str_X18F1_CA | 0.00000 | 0.04738 | i | 233, 0, 0 | Act_Identity | |
234 | | Input_Str_X18F1_Cd | 0.00000 | 0.23713 | i | 234, 0, 0 | Act_Identity | |
235 | | Input_Str_X18F1_CD | 0.00000 | -0.15337 | i | 235, 0, 0 | Act_Identity | |
236 | | Input_Str_X18F1_Co | 0.00000 | 0.08494 | i | 236, 0, 0 | Act_Identity | |
237 | | Input_Str_X18F1_CO | 0.00000 | -0.26108 | i | 237, 0, 0 | Act_Identity | |
238 | | Input_Str_X18F1_Cu | 0.00000 | -0.10676 | i | 238, 0, 0 | Act_Identity | |
239 | | Input_Str_X18F1_CU | 0.00000 | 0.16444 | i | 239, 0, 0 | Act_Identity | |
240 | | Input_Str_X18F1_Fe | 0.00000 | 0.01832 | i | 240, 0, 0 | Act_Identity | |
241 | | Input_Str_X18F1_FE | 0.00000 | 0.17879 | i | 241, 0, 0 | Act_Identity | |
242 | | Input_Str_X18F1_K | 0.00000 | -0.26541 | i | 242, 0, 0 | Act_Identity | |
243 | | Input_Str_X18F1_Mg | 0.00000 | -0.22378 | i | 243, 0, 0 | Act_Identity | |
244 | | Input_Str_X18F1_MG | 0.00000 | 0.21085 | i | 244, 0, 0 | Act_Identity | |
245 | | Input_Str_X18F1_Mn | 0.00000 | 0.21126 | i | 245, 0, 0 | Act_Identity | |
246 | | Input_Str_X18F1_MN | 0.00000 | 0.02897 | i | 246, 0, 0 | Act_Identity | |
247 | | Input_Str_X18F1_Mo | 0.00000 | 0.09827 | i | 247, 0, 0 | Act_Identity | |
248 | | Input_Str_X18F1_MO | 0.00000 | -0.13311 | i | 248, 0, 0 | Act_Identity | |
249 | | Input_Str_X18F1_Na | 0.00000 | 0.15564 | i | 249, 0, 0 | Act_Identity | |
250 | | Input_Str_X18F1_NA | 0.00000 | -0.07242 | i | 250, 0, 0 | Act_Identity | |
251 | | Input_Str_X18F1_Ni | 0.00000 | -0.19884 | i | 251, 0, 0 | Act_Identity | |
252 | | Input_Str_X18F1_NI | 0.00000 | -0.10409 | i | 252, 0, 0 | Act_Identity | |
253 | | Input_Str_X18F1_P | 0.00000 | 0.15706 | i | 253, 0, 0 | Act_Identity | |
254 | | Input_Str_X18F1_Pb | 0.00000 | 0.28992 | i | 254, 0, 0 | Act_Identity | |
255 | | Input_Str_X18F1_PB | 0.00000 | 0.16379 | i | 255, 0, 0 | Act_Identity | |
256 | | Input_Str_X18F1_S | 0.00000 | -0.27452 | i | 256, 0, 0 | Act_Identity | |
257 | | Input_Str_X18F1_Se | 0.00000 | -0.13123 | i | 257, 0, 0 | Act_Identity | |
258 | | Input_Str_X18F1_SE | 0.00000 | -0.10327 | i | 258, 0, 0 | Act_Identity | |
259 | | Input_Str_X18F1_Zn | 0.00000 | -0.25151 | i | 259, 0, 0 | Act_Identity | |
260 | | Input_Str_X18F1_ZN | 0.00000 | 0.12229 | i | 260, 0, 0 | Act_Identity | |
261 | | Input_Str_X18I1_CA | 0.00000 | 0.24555 | i | 261, 0, 0 | Act_Identity | |
262 | | Input_Str_X18I1_MG | 0.00000 | -0.27229 | i | 262, 0, 0 | Act_Identity | |
263 | | Input_Str_X18I1_NA | 0.00000 | 0.22797 | i | 263, 0, 0 | Act_Identity | |
264 | | Input_Str_X18I1_S | 0.00000 | 0.26858 | i | 264, 0, 0 | Act_Identity | |
265 | | Input_Str_X19_COL | 0.00000 | -0.07271 | i | 265, 0, 0 | Act_Identity | |
266 | | Input_Str_X19A1 | 0.00000 | -0.15462 | i | 266, 0, 0 | Act_Identity | |
267 | | Input_Str_X19B_NR | 0.00000 | 0.10855 | i | 267, 0, 0 | Act_Identity | |
268 | | Input_Str_X19B1 | 0.00000 | 0.01496 | i | 268, 0, 0 | Act_Identity | |
269 | | Input_Str_X19B2 | 0.00000 | 0.16468 | i | 269, 0, 0 | Act_Identity | |
270 | | Input_Str_X19F1 | 0.00000 | 0.24543 | i | 270, 0, 0 | Act_Identity | |
271 | | Input_Str_X19F1b | 0.00000 | 0.13452 | i | 271, 0, 0 | Act_Identity | |
272 | | Input_Str_X2.00E.01 | 0.00000 | 0.14608 | i | 272, 0, 0 | Act_Identity | |
273 | | Input_Str_X2.00E.02 | 0.00000 | 0.00856 | i | 273, 0, 0 | Act_Identity | |
274 | | Input_Str_X2_LOI | 0.00000 | 0.12818 | i | 274, 0, 0 | Act_Identity | |
275 | | Input_Str_X2A1 | 0.00000 | 0.18787 | i | 275, 0, 0 | Act_Identity | |
276 | | Input_Str_X2D1 | 0.00000 | -0.16172 | i | 276, 0, 0 | Act_Identity | |
277 | | Input_Str_X2Z1_R1 | 0.00000 | -0.02057 | i | 277, 0, 0 | Act_Identity | |
278 | | Input_Str_X2Z1_R2 | 0.00000 | 0.26428 | i | 278, 0, 0 | Act_Identity | |
279 | | Input_Str_X2Z2_C | 0.00000 | 0.29825 | i | 279, 0, 0 | Act_Identity | |
280 | | Input_Str_X2Z2_CLAY | 0.00000 | -0.09010 | i | 280, 0, 0 | Act_Identity | |
281 | | Input_Str_X2Z2_CS | 0.00000 | 0.13086 | i | 281, 0, 0 | Act_Identity | |
282 | | Input_Str_X2Z2_FS | 0.00000 | 0.14598 | i | 282, 0, 0 | Act_Identity | |
283 | | Input_Str_X2Z2_S | 0.00000 | 0.00456 | i | 283, 0, 0 | Act_Identity | |
284 | | Input_Str_X2Z2_Z | 0.00000 | -0.04798 | i | 284, 0, 0 | Act_Identity | |
285 | | Input_Str_X3_C_B | 0.00000 | -0.16611 | i | 285, 0, 0 | Act_Identity | |
286 | | Input_Str_X3_NR | 0.00000 | -0.16652 | i | 286, 0, 0 | Act_Identity | |
287 | | Input_Str_X3A_C_2.5 | 0.00000 | 0.05695 | i | 287, 0, 0 | Act_Identity | |
288 | | Input_Str_X3A_TSS | 0.00000 | 0.24836 | i | 288, 0, 0 | Act_Identity | |
289 | | Input_Str_X3A1 | 0.00074 | 0.20143 | i | 289, 0, 0 | Act_Identity | |
290 | | Input_Str_X4_NR | 0.00000 | -0.23983 | i | 290, 0, 0 | Act_Identity | |
291 | | Input_Str_X4A_C_1 | 0.00000 | -0.13716 | i | 291, 0, 0 | Act_Identity | |
292 | | Input_Str_X4A_C_2.5 | 0.00000 | 0.24128 | i | 292, 0, 0 | Act_Identity | |
293 | | Input_Str_X4A1 | 0.06600 | 0.12209 | i | 293, 0, 0 | Act_Identity | |
294 | | Input_Str_X4A1_MCLW | 0.00000 | 0.04535 | i | 294, 0, 0 | Act_Identity | |
295 | | Input_Str_X4B_AL | 0.00000 | -0.26954 | i | 295, 0, 0 | Act_Identity | |
296 | | Input_Str_X4B_AL_NR | 0.00000 | 0.23837 | i | 296, 0, 0 | Act_Identity | |
297 | | Input_Str_X4B_C_2.5 | 0.00000 | 0.09999 | i | 297, 0, 0 | Act_Identity | |
298 | | Input_Str_X4B1 | 0.43279 | -0.07656 | i | 298, 0, 0 | Act_Identity | |
299 | | Input_Str_X4B2 | 0.00000 | 0.19046 | i | 299, 0, 0 | Act_Identity | |
300 | | Input_Str_X4B4 | 0.00000 | 0.10145 | i | 300, 0, 0 | Act_Identity | |
301 | | Input_Str_X4B5_MCLW | 0.00000 | -0.28426 | i | 301, 0, 0 | Act_Identity | |
302 | | Input_Str_X4C_C_1 | 0.00000 | 0.14832 | i | 302, 0, 0 | Act_Identity | |
303 | | Input_Str_X4C1 | 0.00000 | 0.18173 | i | 303, 0, 0 | Act_Identity | |
304 | | Input_Str_X4G_NR | 0.00000 | 0.19335 | i | 304, 0, 0 | Act_Identity | |
305 | | Input_Str_X5_C_B | 0.00000 | 0.16698 | i | 305, 0, 0 | Act_Identity | |
306 | | Input_Str_X5_NR | 0.00000 | 0.00593 | i | 306, 0, 0 | Act_Identity | |
307 | | Input_Str_X5A_C_2.5 | 0.00000 | -0.19869 | i | 307, 0, 0 | Act_Identity | |
308 | | Input_Str_X5A_NR | 0.18092 | -0.00582 | i | 308, 0, 0 | Act_Identity | |
309 | | Input_Str_X5A1 | 0.00000 | -0.29797 | i | 309, 0, 0 | Act_Identity | |
310 | | Input_Str_X5A2 | 0.00000 | -0.23626 | i | 310, 0, 0 | Act_Identity | |
311 | | Input_Str_X5A2b | 0.00000 | 0.06984 | i | 311, 0, 0 | Act_Identity | |
312 | | Input_Str_X6_DC | 0.00000 | 0.00562 | i | 312, 0, 0 | Act_Identity | |
313 | | Input_Str_X6A1 | 0.00000 | -0.15119 | i | 313, 0, 0 | Act_Identity | |
314 | | Input_Str_X6A1_UC | 0.00000 | 0.09990 | i | 314, 0, 0 | Act_Identity | |
315 | | Input_Str_X6B1 | 0.00000 | 0.22774 | i | 315, 0, 0 | Act_Identity | |
316 | | Input_Str_X6B2 | 0.00000 | -0.27342 | i | 316, 0, 0 | Act_Identity | |
317 | | Input_Str_X6B2b | 0.00000 | 0.24611 | i | 317, 0, 0 | Act_Identity | |
318 | | Input_Str_X6B3 | 0.00000 | -0.14571 | i | 318, 0, 0 | Act_Identity | |
319 | | Input_Str_X6B3a | 0.00000 | 0.06527 | i | 319, 0, 0 | Act_Identity | |
320 | | Input_Str_X6B3b | 0.00000 | 0.17293 | i | 320, 0, 0 | Act_Identity | |
321 | | Input_Str_X6B4_0_30 | 0.00000 | -0.22618 | i | 321, 0, 0 | Act_Identity | |
322 | | Input_Str_X6B4_30_100 | 0.00000 | -0.10631 | i | 322, 0, 0 | Act_Identity | |
323 | | Input_Str_X6H1_HOC | 0.00000 | 0.17875 | i | 323, 0, 0 | Act_Identity | |
324 | | Input_Str_X6H1_POC | 0.00000 | 0.09509 | i | 324, 0, 0 | Act_Identity | |
325 | | Input_Str_X6H1_ROC | 0.00000 | -0.19154 | i | 325, 0, 0 | Act_Identity | |
326 | | Input_Str_X6H1_TOC | 0.00000 | -0.00267 | i | 326, 0, 0 | Act_Identity | |
327 | | Input_Str_X6H2a | 0.00000 | -0.14743 | i | 327, 0, 0 | Act_Identity | |
328 | | Input_Str_X6H2b | 0.00000 | 0.15428 | i | 328, 0, 0 | Act_Identity | |
329 | | Input_Str_X6H2c | 0.00000 | 0.15885 | i | 329, 0, 0 | Act_Identity | |
330 | | Input_Str_X6H3 | 0.00000 | 0.09468 | i | 330, 0, 0 | Act_Identity | |
331 | | Input_Str_X6H3_0_30 | 0.00000 | -0.05155 | i | 331, 0, 0 | Act_Identity | |
332 | | Input_Str_X6H3_30_100 | 0.00000 | -0.18413 | i | 332, 0, 0 | Act_Identity | |
333 | | Input_Str_X6Z | 0.00000 | -0.16155 | i | 333, 0, 0 | Act_Identity | |
334 | | Input_Str_X7_C_B | 0.00000 | 0.04257 | i | 334, 0, 0 | Act_Identity | |
335 | | Input_Str_X7_NR | 0.00000 | 0.19296 | i | 335, 0, 0 | Act_Identity | |
336 | | Input_Str_X7A1 | 0.00000 | 0.25130 | i | 336, 0, 0 | Act_Identity | |
337 | | Input_Str_X7A2 | 0.00000 | -0.23645 | i | 337, 0, 0 | Act_Identity | |
338 | | Input_Str_X7A2a | 0.00000 | -0.29663 | i | 338, 0, 0 | Act_Identity | |
339 | | Input_Str_X7A5 | 0.00000 | -0.02956 | i | 339, 0, 0 | Act_Identity | |
340 | | Input_Str_X7A6b_MCLW | 0.00000 | 0.13405 | i | 340, 0, 0 | Act_Identity | |
341 | | Input_Str_X7B1 | 0.00000 | -0.13943 | i | 341, 0, 0 | Act_Identity | |
342 | | Input_Str_X7C_1MKCla | 0.00000 | 0.10766 | i | 342, 0, 0 | Act_Identity | |
343 | | Input_Str_X7C_1MKClb | 0.00000 | 0.16769 | i | 343, 0, 0 | Act_Identity | |
344 | | Input_Str_X7C_CASO4 | 0.00000 | 0.01157 | i | 344, 0, 0 | Act_Identity | |
345 | | Input_Str_X7C1 | 0.00000 | -0.16012 | i | 345, 0, 0 | Act_Identity | |
346 | | Input_Str_X7C1a | 0.00000 | 0.11384 | i | 346, 0, 0 | Act_Identity | |
347 | | Input_Str_X7C1b | 0.00000 | -0.19148 | i | 347, 0, 0 | Act_Identity | |
348 | | Input_Str_X7C1d | 0.00000 | -0.17012 | i | 348, 0, 0 | Act_Identity | |
349 | | Input_Str_X7C1e | 0.00000 | 0.10239 | i | 349, 0, 0 | Act_Identity | |
350 | | Input_Str_X7C2b | 0.00000 | -0.19299 | i | 350, 0, 0 | Act_Identity | |
351 | | Input_Str_X7C2b_NH4 | 0.00000 | -0.17238 | i | 351, 0, 0 | Act_Identity | |
352 | | Input_Str_X7C2b_NO3 | 0.00000 | 0.24135 | i | 352, 0, 0 | Act_Identity | |
353 | | Input_Str_X7D1a | 0.00000 | 0.10899 | i | 353, 0, 0 | Act_Identity | |
354 | | Input_Str_X7E1a | 0.00000 | -0.05777 | i | 354, 0, 0 | Act_Identity | |
355 | | Input_Str_X7E1b | 0.00000 | -0.05967 | i | 355, 0, 0 | Act_Identity | |
356 | | Input_Str_X8A1 | 0.00000 | 0.12040 | i | 356, 0, 0 | Act_Identity | |
357 | | Input_Str_X9.00E.02 | 0.00000 | -0.07461 | i | 357, 0, 0 | Act_Identity | |
358 | | Input_Str_X9_E_NR | 0.00000 | -0.00632 | i | 358, 0, 0 | Act_Identity | |
359 | | Input_Str_X9_NR | 0.00000 | -0.11431 | i | 359, 0, 0 | Act_Identity | |
360 | | Input_Str_X9A_HCL | 0.00000 | 0.09883 | i | 360, 0, 0 | Act_Identity | |
361 | | Input_Str_X9A_HCLP2O5 | 0.00000 | -0.17929 | i | 361, 0, 0 | Act_Identity | |
362 | | Input_Str_X9A_HF. | 0.00000 | -0.14511 | i | 362, 0, 0 | Act_Identity | |
363 | | Input_Str_X9A_NR | 0.00000 | 0.15594 | i | 363, 0, 0 | Act_Identity | |
364 | | Input_Str_X9A_S14 | 0.00000 | 0.02702 | i | 364, 0, 0 | Act_Identity | |
365 | | Input_Str_X9A1 | 0.00000 | -0.07142 | i | 365, 0, 0 | Act_Identity | |
366 | | Input_Str_X9A3 | 0.00000 | 0.15612 | i | 366, 0, 0 | Act_Identity | |
367 | | Input_Str_X9A3a | 0.00000 | 0.11863 | i | 367, 0, 0 | Act_Identity | |
368 | | Input_Str_X9B | 0.00000 | -0.02621 | i | 368, 0, 0 | Act_Identity | |
369 | | Input_Str_X9B_9C | 0.00000 | -0.03746 | i | 369, 0, 0 | Act_Identity | |
370 | | Input_Str_X9B_NR | 0.00000 | 0.18736 | i | 370, 0, 0 | Act_Identity | |
371 | | Input_Str_X9B1 | 0.00000 | -0.00874 | i | 371, 0, 0 | Act_Identity | |
372 | | Input_Str_X9B2 | 0.00000 | -0.09678 | i | 372, 0, 0 | Act_Identity | |
373 | | Input_Str_X9B2_COL | 0.00000 | 0.03574 | i | 373, 0, 0 | Act_Identity | |
374 | | Input_Str_X9BUFF_0 | 0.00000 | -0.20123 | i | 374, 0, 0 | Act_Identity | |
375 | | Input_Str_X9BUFF_0.5 | 0.00000 | -0.13143 | i | 375, 0, 0 | Act_Identity | |
376 | | Input_Str_X9BUFF_1 | 0.00000 | -0.20888 | i | 376, 0, 0 | Act_Identity | |
377 | | Input_Str_X9BUFF_2 | 0.00000 | 0.14822 | i | 377, 0, 0 | Act_Identity | |
378 | | Input_Str_X9BUFF_4 | 0.00000 | -0.20079 | i | 378, 0, 0 | Act_Identity | |
379 | | Input_Str_X9C1 | 0.00000 | 0.23068 | i | 379, 0, 0 | Act_Identity | |
380 | | Input_Str_X9C2 | 0.00000 | 0.23493 | i | 380, 0, 0 | Act_Identity | |
381 | | Input_Str_X9D2 | 0.00000 | -0.07770 | i | 381, 0, 0 | Act_Identity | |
382 | | Input_Str_X9E | 0.00000 | -0.11083 | i | 382, 0, 0 | Act_Identity | |
383 | | Input_Str_X9G_BSES | 0.00000 | 0.26545 | i | 383, 0, 0 | Act_Identity | |
384 | | Input_Str_X9G1 | 0.00000 | -0.11128 | i | 384, 0, 0 | Act_Identity | |
385 | | Input_Str_X9G2 | 0.00000 | 0.09934 | i | 385, 0, 0 | Act_Identity | |
386 | | Input_Str_X9H_NR | 0.00000 | 0.08191 | i | 386, 0, 0 | Act_Identity | |
387 | | Input_Str_X9H1 | 0.00000 | -0.27549 | i | 387, 0, 0 | Act_Identity | |
388 | | Input_Str_X9I1 | 0.00000 | -0.14915 | i | 388, 0, 0 | Act_Identity | |
389 | | Input_Str_X9I2b | 0.00000 | 0.28272 | i | 389, 0, 0 | Act_Identity | |
390 | | Input_Str_X9I2B | 0.00000 | 0.11530 | i | 390, 0, 0 | Act_Identity | |
391 | | Input_Str_X9J2 | 0.00000 | 0.25988 | i | 391, 0, 0 | Act_Identity | |
392 | | Input_Str_X9R1 | 0.00000 | 0.01372 | i | 392, 0, 0 | Act_Identity | |
393 | | Input_Str_M1a | 0.00000 | 0.10949 | i | 393, 0, 0 | Act_Identity | |
394 | | Input_Str_MIN_EC | 0.00000 | -0.03701 | i | 394, 0, 0 | Act_Identity | |
395 | | Input_Str_MIN_NR_K2O | 0.00000 | 0.27564 | i | 395, 0, 0 | Act_Identity | |
396 | | Input_Str_P10_1m2m | 0.00000 | 0.20976 | i | 396, 0, 0 | Act_Identity | |
397 | | Input_Str_P10_20_100 | 0.00000 | 0.09867 | i | 397, 0, 0 | Act_Identity | |
398 | | Input_Str_P10_20_75 | 0.00000 | 0.26268 | i | 398, 0, 0 | Act_Identity | |
399 | | Input_Str_P10_20_75a | 0.00000 | -0.22437 | i | 399, 0, 0 | Act_Identity | |
400 | | Input_Str_P10_75_106 | 0.00000 | 0.15216 | i | 400, 0, 0 | Act_Identity | |
401 | | Input_Str_P10_C_MCLW | 0.00000 | 0.03396 | i | 401, 0, 0 | Act_Identity | |
402 | | Input_Str_P10_CF_C | 0.00000 | 0.13994 | i | 402, 0, 0 | Act_Identity | |
403 | | Input_Str_P10_CF_CS | 0.00000 | 0.25416 | i | 403, 0, 0 | Act_Identity | |
404 | | Input_Str_P10_CF_FS | 0.00000 | -0.00688 | i | 404, 0, 0 | Act_Identity | |
405 | | Input_Str_P10_CF_S | 0.00000 | -0.23783 | i | 405, 0, 0 | Act_Identity | |
406 | | Input_Str_P10_CF_Z | 0.00000 | -0.11808 | i | 406, 0, 0 | Act_Identity | |
407 | | Input_Str_P10_GRAV | 0.00000 | -0.22799 | i | 407, 0, 0 | Act_Identity | |
408 | | Input_Str_P10_gt2m | 0.00000 | 0.29885 | i | 408, 0, 0 | Act_Identity | |
409 | | Input_Str_P10_gt2MI | 0.00000 | 0.23990 | i | 409, 0, 0 | Act_Identity | |
410 | | Input_Str_P10_gt2OM | 0.00000 | -0.27747 | i | 410, 0, 0 | Act_Identity | |
411 | | Input_Str_P10_HYD_C | 0.00000 | -0.03365 | i | 411, 0, 0 | Act_Identity | |
412 | | Input_Str_P10_HYD_CS | 0.00000 | -0.17573 | i | 412, 0, 0 | Act_Identity | |
413 | | Input_Str_P10_HYD_FS | 0.00000 | 0.02001 | i | 413, 0, 0 | Act_Identity | |
414 | | Input_Str_P10_HYD_S | 0.00000 | -0.09668 | i | 414, 0, 0 | Act_Identity | |
415 | | Input_Str_P10_HYD_Z | 0.00000 | -0.00714 | i | 415, 0, 0 | Act_Identity | |
416 | | Input_Str_P10_I_C | 0.00000 | 0.29441 | i | 416, 0, 0 | Act_Identity | |
417 | | Input_Str_P10_I_CS | 0.00000 | -0.19101 | i | 417, 0, 0 | Act_Identity | |
418 | | Input_Str_P10_I_FS | 0.00000 | -0.17668 | i | 418, 0, 0 | Act_Identity | |
419 | | Input_Str_P10_I_S | 0.00000 | -0.16519 | i | 419, 0, 0 | Act_Identity | |
420 | | Input_Str_P10_I_Z | 0.00000 | 0.17005 | i | 420, 0, 0 | Act_Identity | |
421 | | Input_Str_P10_NR_C | 0.00000 | -0.14017 | i | 421, 0, 0 | Act_Identity | |
422 | | Input_Str_P10_NR_CS | 0.00000 | -0.29636 | i | 422, 0, 0 | Act_Identity | |
423 | | Input_Str_P10_NR_FS | 0.00000 | 0.11383 | i | 423, 0, 0 | Act_Identity | |
424 | | Input_Str_P10_NR_S | 0.00000 | -0.21335 | i | 424, 0, 0 | Act_Identity | |
425 | | Input_Str_P10_NR_Saa | 0.00000 | 0.16785 | i | 425, 0, 0 | Act_Identity | |
426 | | Input_Str_P10_NR_Z | 0.00000 | -0.27570 | i | 426, 0, 0 | Act_Identity | |
427 | | Input_Str_P10_NR_ZC | 0.00000 | -0.26228 | i | 427, 0, 0 | Act_Identity | |
428 | | Input_Str_P10_PB_C | 0.00000 | -0.17169 | i | 428, 0, 0 | Act_Identity | |
429 | | Input_Str_P10_PB_CS | 0.00000 | -0.27478 | i | 429, 0, 0 | Act_Identity | |
430 | | Input_Str_P10_PB_FS | 0.00000 | 0.02394 | i | 430, 0, 0 | Act_Identity | |
431 | | Input_Str_P10_PB_S | 0.00000 | -0.14194 | i | 431, 0, 0 | Act_Identity | |
432 | | Input_Str_P10_PB_Z | 0.00000 | -0.27156 | i | 432, 0, 0 | Act_Identity | |
433 | | Input_Str_P10_PB1_C | 0.00000 | -0.02061 | i | 433, 0, 0 | Act_Identity | |
434 | | Input_Str_P10_PB1_CS | 0.00000 | 0.04408 | i | 434, 0, 0 | Act_Identity | |
435 | | Input_Str_P10_PB1_FS | 0.00000 | 0.26331 | i | 435, 0, 0 | Act_Identity | |
436 | | Input_Str_P10_PB1_Z | 0.00000 | -0.19597 | i | 436, 0, 0 | Act_Identity | |
437 | | Input_Str_P10_S_0.20 | 0.00000 | 0.21131 | i | 437, 0, 0 | Act_Identity | |
438 | | Input_Str_P10_S_0.48 | 0.00000 | -0.21730 | i | 438, 0, 0 | Act_Identity | |
439 | | Input_Str_P10_S_1 | 0.00000 | -0.01211 | i | 439, 0, 0 | Act_Identity | |
440 | | Input_Str_P10_S_1000 | 0.00000 | -0.27435 | i | 440, 0, 0 | Act_Identity | |
441 | | Input_Str_P10_S_125 | 0.00000 | -0.05646 | i | 441, 0, 0 | Act_Identity | |
442 | | Input_Str_P10_S_15.6 | 0.00000 | 0.05901 | i | 442, 0, 0 | Act_Identity | |
443 | | Input_Str_P10_S_2 | 0.00000 | -0.14140 | i | 443, 0, 0 | Act_Identity | |
444 | | Input_Str_P10_S_20 | 0.00000 | 0.23696 | i | 444, 0, 0 | Act_Identity | |
445 | | Input_Str_P10_S_2000 | 0.00000 | -0.07020 | i | 445, 0, 0 | Act_Identity | |
446 | | Input_Str_P10_S_250 | 0.00000 | -0.16875 | i | 446, 0, 0 | Act_Identity | |
447 | | Input_Str_P10_S_3.9 | 0.00000 | 0.07389 | i | 447, 0, 0 | Act_Identity | |
448 | | Input_Str_P10_S_31.2 | 0.00000 | -0.14154 | i | 448, 0, 0 | Act_Identity | |
449 | | Input_Str_P10_S_500 | 0.00000 | -0.00841 | i | 449, 0, 0 | Act_Identity | |
450 | | Input_Str_P10_S_53 | 0.00000 | -0.27429 | i | 450, 0, 0 | Act_Identity | |
451 | | Input_Str_P10_S_63 | 0.00000 | -0.10398 | i | 451, 0, 0 | Act_Identity | |
452 | | Input_Str_P10_S_7.8 | 0.00000 | -0.06352 | i | 452, 0, 0 | Act_Identity | |
453 | | Input_Str_P10_S_MCLW | 0.00000 | 0.20917 | i | 453, 0, 0 | Act_Identity | |
454 | | Input_Str_P10_Z_MCLW | 0.00000 | -0.24524 | i | 454, 0, 0 | Act_Identity | |
455 | | Input_Str_P10100_200 | 0.00000 | -0.09693 | i | 455, 0, 0 | Act_Identity | |
456 | | Input_Str_P10106_150 | 0.00000 | 0.18911 | i | 456, 0, 0 | Act_Identity | |
457 | | Input_Str_P10150_180 | 0.00000 | -0.12823 | i | 457, 0, 0 | Act_Identity | |
458 | | Input_Str_P10180_300 | 0.00000 | 0.11000 | i | 458, 0, 0 | Act_Identity | |
459 | | Input_Str_P10200_500 | 0.00000 | 0.25947 | i | 459, 0, 0 | Act_Identity | |
460 | | Input_Str_P10200_600 | 0.00000 | -0.18610 | i | 460, 0, 0 | Act_Identity | |
461 | | Input_Str_P102002000 | 0.00000 | 0.04132 | i | 461, 0, 0 | Act_Identity | |
462 | | Input_Str_P10300_600 | 0.00000 | -0.14712 | i | 462, 0, 0 | Act_Identity | |
463 | | Input_Str_P105002000 | 0.00000 | -0.05468 | i | 463, 0, 0 | Act_Identity | |
464 | | Input_Str_P106001000 | 0.00000 | 0.19792 | i | 464, 0, 0 | Act_Identity | |
465 | | Input_Str_P106002000 | 0.00000 | 0.17128 | i | 465, 0, 0 | Act_Identity | |
466 | | Input_Str_P10A1_C | 0.00000 | -0.19758 | i | 466, 0, 0 | Act_Identity | |
467 | | Input_Str_P10A1_CS | 0.00000 | 0.21477 | i | 467, 0, 0 | Act_Identity | |
468 | | Input_Str_P10A1_FS | 0.00000 | 0.01604 | i | 468, 0, 0 | Act_Identity | |
469 | | Input_Str_P10A1_Z | 0.00000 | -0.24927 | i | 469, 0, 0 | Act_Identity | |
470 | | Input_Str_P3A_CLW | 0.00000 | -0.12071 | i | 470, 0, 0 | Act_Identity | |
471 | | Input_Str_P3A_NR | 0.00000 | 0.02230 | i | 471, 0, 0 | Act_Identity | |
472 | | Input_Str_P3A1 | 0.00000 | 0.00281 | i | 472, 0, 0 | Act_Identity | |
473 | | Input_Str_P3A1_C4 | 0.00000 | 0.02532 | i | 473, 0, 0 | Act_Identity | |
474 | | Input_Str_P3A1_CLOD | 0.00000 | -0.08893 | i | 474, 0, 0 | Act_Identity | |
475 | | Input_Str_P3A1_e | 0.00000 | 0.26154 | i | 475, 0, 0 | Act_Identity | |
476 | | Input_Str_P3A2_McK | 0.00000 | 0.07468 | i | 476, 0, 0 | Act_Identity | |
477 | | Input_Str_P3A2_McKMP | 0.00000 | -0.26197 | i | 477, 0, 0 | Act_Identity | |
478 | | Input_Str_P3B_GV_01 | 0.00000 | 0.03701 | i | 478, 0, 0 | Act_Identity | |
479 | | Input_Str_P3B_GV_03 | 0.00000 | -0.11953 | i | 479, 0, 0 | Act_Identity | |
480 | | Input_Str_P3B_GV_15 | 0.00000 | 0.06822 | i | 480, 0, 0 | Act_Identity | |
481 | | Input_Str_P3B_NR_005 | 0.00000 | 0.04719 | i | 481, 0, 0 | Act_Identity | |
482 | | Input_Str_P3B_NR_01 | 0.00000 | 0.19408 | i | 482, 0, 0 | Act_Identity | |
483 | | Input_Str_P3B_NR_15 | 0.00000 | -0.13506 | i | 483, 0, 0 | Act_Identity | |
484 | | Input_Str_P3B_VL_01 | 0.00000 | 0.20188 | i | 484, 0, 0 | Act_Identity | |
485 | | Input_Str_P3B_VL_15 | 0.00000 | 0.08572 | i | 485, 0, 0 | Act_Identity | |
486 | | Input_Str_P3B1GV_15 | 0.00000 | -0.13654 | i | 486, 0, 0 | Act_Identity | |
487 | | Input_Str_P3B1VL_1 | 0.00000 | -0.29106 | i | 487, 0, 0 | Act_Identity | |
488 | | Input_Str_P3B1VL_15 | 0.00000 | -0.24934 | i | 488, 0, 0 | Act_Identity | |
489 | | Input_Str_P3B2GV_1 | 0.00000 | 0.28188 | i | 489, 0, 0 | Act_Identity | |
490 | | Input_Str_P3B2GV_15 | 0.00000 | -0.06711 | i | 490, 0, 0 | Act_Identity | |
491 | | Input_Str_P3B2GV_5 | 0.00000 | -0.05753 | i | 491, 0, 0 | Act_Identity | |
492 | | Input_Str_P3B2VL_03 | 0.00000 | -0.08402 | i | 492, 0, 0 | Act_Identity | |
493 | | Input_Str_P3B2VL_1 | 0.00000 | -0.08936 | i | 493, 0, 0 | Act_Identity | |
494 | | Input_Str_P3B2VL_15 | 0.00000 | 0.15319 | i | 494, 0, 0 | Act_Identity | |
495 | | Input_Str_P3B2VL_5 | 0.00000 | 0.21923 | i | 495, 0, 0 | Act_Identity | |
496 | | Input_Str_P3B3VLa001 | 0.00000 | 0.27858 | i | 496, 0, 0 | Act_Identity | |
497 | | Input_Str_P3B3VLa005 | 0.00000 | -0.05305 | i | 497, 0, 0 | Act_Identity | |
498 | | Input_Str_P3B3VLa01 | 0.00000 | -0.27604 | i | 498, 0, 0 | Act_Identity | |
499 | | Input_Str_P3B3VLa03 | 0.00000 | -0.15086 | i | 499, 0, 0 | Act_Identity | |
500 | | Input_Str_P3B3VLa06 | 0.00000 | -0.01597 | i | 500, 0, 0 | Act_Identity | |
501 | | Input_Str_P3B3VLaSAT | 0.00000 | -0.02706 | i | 501, 0, 0 | Act_Identity | |
502 | | Input_Str_P3B3VLb001 | 0.00000 | 0.02713 | i | 502, 0, 0 | Act_Identity | |
503 | | Input_Str_P3B3VLb003 | 0.00000 | -0.16229 | i | 503, 0, 0 | Act_Identity | |
504 | | Input_Str_P3B3VLb005 | 0.00000 | 0.13532 | i | 504, 0, 0 | Act_Identity | |
505 | | Input_Str_P3B3VLb01 | 0.00000 | -0.28005 | i | 505, 0, 0 | Act_Identity | |
506 | | Input_Str_P3B3VLb03 | 0.00000 | 0.19038 | i | 506, 0, 0 | Act_Identity | |
507 | | Input_Str_P3B3VLb05 | 0.00000 | -0.13637 | i | 507, 0, 0 | Act_Identity | |
508 | | Input_Str_P3B3VLb06 | 0.00000 | -0.25314 | i | 508, 0, 0 | Act_Identity | |
509 | | Input_Str_P3B3VLbSAT | 0.00000 | 0.15193 | i | 509, 0, 0 | Act_Identity | |
510 | | Input_Str_P3B3VLc001 | 0.00000 | 0.04044 | i | 510, 0, 0 | Act_Identity | |
511 | | Input_Str_P3B3VLc003 | 0.00000 | -0.18439 | i | 511, 0, 0 | Act_Identity | |
512 | | Input_Str_P3B3VLc005 | 0.00000 | 0.15625 | i | 512, 0, 0 | Act_Identity | |
513 | | Input_Str_P3B3VLc01 | 0.00000 | -0.13435 | i | 513, 0, 0 | Act_Identity | |
514 | | Input_Str_P3B3VLc03 | 0.00000 | -0.28751 | i | 514, 0, 0 | Act_Identity | |
515 | | Input_Str_P3B3VLc06 | 0.00000 | -0.00060 | i | 515, 0, 0 | Act_Identity | |
516 | | Input_Str_P3B3VLcSAT | 0.00000 | 0.19131 | i | 516, 0, 0 | Act_Identity | |
517 | | Input_Str_P3B3VLd06 | 0.00000 | 0.22852 | i | 517, 0, 0 | Act_Identity | |
518 | | Input_Str_P3B3VLd1 | 0.00000 | -0.12356 | i | 518, 0, 0 | Act_Identity | |
519 | | Input_Str_P3B3VLd15 | 0.00000 | 0.29312 | i | 519, 0, 0 | Act_Identity | |
520 | | Input_Str_P3B3VLd3 | 0.00000 | 0.24040 | i | 520, 0, 0 | Act_Identity | |
521 | | Input_Str_P3B3VLd5 | 0.00000 | 0.09911 | i | 521, 0, 0 | Act_Identity | |
522 | | Input_Str_P3B3VLe004 | 0.00000 | -0.16283 | i | 522, 0, 0 | Act_Identity | |
523 | | Input_Str_P3B3VLe01 | 0.00000 | 0.01344 | i | 523, 0, 0 | Act_Identity | |
524 | | Input_Str_P3B3VLe03 | 0.00000 | -0.14822 | i | 524, 0, 0 | Act_Identity | |
525 | | Input_Str_P3B3VLe06 | 0.00000 | -0.02906 | i | 525, 0, 0 | Act_Identity | |
526 | | Input_Str_P3B3VLe15 | 0.00000 | -0.21307 | i | 526, 0, 0 | Act_Identity | |
527 | | Input_Str_P3B3VLe2 | 0.00000 | -0.24540 | i | 527, 0, 0 | Act_Identity | |
528 | | Input_Str_P3B3VLe7 | 0.00000 | -0.09232 | i | 528, 0, 0 | Act_Identity | |
529 | | Input_Str_P3B4GV_01 | 0.00000 | -0.14617 | i | 529, 0, 0 | Act_Identity | |
530 | | Input_Str_P3B4VL_005 | 0.00000 | 0.17670 | i | 530, 0, 0 | Act_Identity | |
531 | | Input_Str_P3B5GV_01 | 0.00000 | 0.15555 | i | 531, 0, 0 | Act_Identity | |
532 | | Input_Str_P3B6VL_DUL | 0.00000 | 0.04197 | i | 532, 0, 0 | Act_Identity | |
533 | | Input_Str_P3B6VL_LL | 0.00000 | 0.20810 | i | 533, 0, 0 | Act_Identity | |
534 | | Input_Str_P3B6VL_SAT | 0.00000 | 0.29816 | i | 534, 0, 0 | Act_Identity | |
535 | | Input_Str_P4_100DMcK | 0.00000 | -0.21589 | i | 535, 0, 0 | Act_Identity | |
536 | | Input_Str_P4_10DMcK | 0.00000 | -0.13006 | i | 536, 0, 0 | Act_Identity | |
537 | | Input_Str_P4_30_LOV | 0.00000 | 0.13913 | i | 537, 0, 0 | Act_Identity | |
538 | | Input_Str_P4_30DMcK | 0.00000 | -0.23203 | i | 538, 0, 0 | Act_Identity | |
539 | | Input_Str_P4_50_McK | 0.00000 | 0.13416 | i | 539, 0, 0 | Act_Identity | |
540 | | Input_Str_P4_50DMcK | 0.00000 | 0.16062 | i | 540, 0, 0 | Act_Identity | |
541 | | Input_Str_P4_sat | 0.00000 | -0.07426 | i | 541, 0, 0 | Act_Identity | |
542 | | Input_Str_P4_sat_FH | 0.00000 | 0.11622 | i | 542, 0, 0 | Act_Identity | |
543 | | Input_Str_P4_sat_For | 0.00000 | -0.05937 | i | 543, 0, 0 | Act_Identity | |
544 | | Input_Str_P4_sat_LOV | 0.00000 | -0.05748 | i | 544, 0, 0 | Act_Identity | |
545 | | Input_Str_P4_sat_McK | 0.00000 | 0.27756 | i | 545, 0, 0 | Act_Identity | |
546 | | Input_Str_P5_COLE | 0.00000 | -0.23580 | i | 546, 0, 0 | Act_Identity | |
547 | | Input_Str_P5_LS_MOD | 0.00000 | -0.23613 | i | 547, 0, 0 | Act_Identity | |
548 | | Input_Str_P6_LP | 0.00000 | -0.24521 | i | 548, 0, 0 | Act_Identity | |
549 | | Input_Str_PWS1.2mm | 0.00000 | 0.00752 | i | 549, 0, 0 | Act_Identity | |
550 | | Input_Str_PWS20.63 | 0.00000 | -0.09446 | i | 550, 0, 0 | Act_Identity | |
551 | | Input_Str_PWS212.425 | 0.00000 | -0.20799 | i | 551, 0, 0 | Act_Identity | |
552 | | Input_Str_PWS425.1mm | 0.00000 | 0.23614 | i | 552, 0, 0 | Act_Identity | |
553 | | Input_Str_PWS63.212 | 0.00000 | 0.27107 | i | 553, 0, 0 | Act_Identity | |
554 | | Input_Str_TE_MIR_AL2O3 | 0.00000 | 0.25217 | i | 554, 0, 0 | Act_Identity | |
555 | | Input_Str_TE_MIR_FE2O3 | 0.00000 | 0.12622 | i | 555, 0, 0 | Act_Identity | |
556 | | Input_Str_TE_MIR_SI02 | 0.00000 | 0.15521 | i | 556, 0, 0 | Act_Identity | |
557 | | Input_Str_TE_NR_AL | 0.00000 | -0.01500 | i | 557, 0, 0 | Act_Identity | |
558 | | Input_Str_TE_NR_AL2O | 0.00000 | 0.28961 | i | 558, 0, 0 | Act_Identity | |
559 | | Input_Str_TE_NR_CA | 0.00000 | 0.03089 | i | 559, 0, 0 | Act_Identity | |
560 | | Input_Str_TE_NR_FE20 | 0.00000 | 0.05745 | i | 560, 0, 0 | Act_Identity | |
561 | | Input_Str_TE_NR_MG | 0.00000 | -0.05308 | i | 561, 0, 0 | Act_Identity | |
562 | | Input_Str_TE_NR_NA | 0.00000 | -0.27849 | i | 562, 0, 0 | Act_Identity | |
563 | | Input_Str_TE_NR_SI02 | 0.00000 | 0.22451 | i | 563, 0, 0 | Act_Identity | |
564 | | Input_Str_TE_NR_TI02 | 0.00000 | -0.25438 | i | 564, 0, 0 | Act_Identity | |
565 | | Input_Str_TE_XRF_MG | 0.00000 | 0.03792 | i | 565, 0, 0 | Act_Identity | |
566 | | Input_Str_TE_XRFAL | 0.00000 | -0.00485 | i | 566, 0, 0 | Act_Identity | |
567 | | Input_Str_TE_XRFCA | 0.00000 | 0.17034 | i | 567, 0, 0 | Act_Identity | |
568 | | Input_Str_TE_XRFNA | 0.00000 | -0.29528 | i | 568, 0, 0 | Act_Identity | |
569 | | Input_Str_TE_XRFSI02 | 0.00000 | 0.21742 | i | 569, 0, 0 | Act_Identity | |
570 | | Input_Str_TE_XRFTIO2 | 0.00000 | -0.28056 | i | 570, 0, 0 | Act_Identity | |
571 | | Input_Str_XRD_C_Amp | 0.00000 | -0.15158 | i | 571, 0, 0 | Act_Identity | |
572 | | Input_Str_XRD_C_An | 0.00000 | -0.23543 | i | 572, 0, 0 | Act_Identity | |
573 | | Input_Str_XRD_C_Bhm | 0.00000 | 0.15474 | i | 573, 0, 0 | Act_Identity | |
574 | | Input_Str_XRD_C_Bt | 0.00000 | -0.10811 | i | 574, 0, 0 | Act_Identity | |
575 | | Input_Str_XRD_C_Cal | 0.00000 | 0.15284 | i | 575, 0, 0 | Act_Identity | |
576 | | Input_Str_XRD_C_Ch2 | 0.00000 | -0.06748 | i | 576, 0, 0 | Act_Identity | |
577 | | Input_Str_XRD_C_Chl | 0.00000 | -0.24407 | i | 577, 0, 0 | Act_Identity | |
578 | | Input_Str_XRD_C_Fsp | 0.00000 | 0.00711 | i | 578, 0, 0 | Act_Identity | |
579 | | Input_Str_XRD_C_Gbs | 0.00000 | 0.17552 | i | 579, 0, 0 | Act_Identity | |
580 | | Input_Str_XRD_C_Gth | 0.00000 | -0.10813 | i | 580, 0, 0 | Act_Identity | |
581 | | Input_Str_XRD_C_Hem | 0.00000 | 0.15674 | i | 581, 0, 0 | Act_Identity | |
582 | | Input_Str_XRD_C_Ht0 | 0.00000 | 0.23457 | i | 582, 0, 0 | Act_Identity | |
583 | | Input_Str_XRD_C_Ilt | 0.00000 | 0.29490 | i | 583, 0, 0 | Act_Identity | |
584 | | Input_Str_XRD_C_Is | 0.00000 | 0.13797 | i | 584, 0, 0 | Act_Identity | |
585 | | Input_Str_XRD_C_K2O | 0.00000 | -0.01415 | i | 585, 0, 0 | Act_Identity | |
586 | | Input_Str_XRD_C_Ka | 0.00000 | 0.06244 | i | 586, 0, 0 | Act_Identity | |
587 | | Input_Str_XRD_C_Kln | 0.00000 | 0.29406 | i | 587, 0, 0 | Act_Identity | |
588 | | Input_Str_XRD_C_Lp | 0.00000 | -0.00403 | i | 588, 0, 0 | Act_Identity | |
589 | | Input_Str_XRD_C_Mag | 0.00000 | 0.00046 | i | 589, 0, 0 | Act_Identity | |
590 | | Input_Str_XRD_C_Mca | 0.00000 | 0.27909 | i | 590, 0, 0 | Act_Identity | |
591 | | Input_Str_XRD_C_Mgh | 0.00000 | 0.16336 | i | 591, 0, 0 | Act_Identity | |
592 | | Input_Str_XRD_C_Mnt | 0.00000 | 0.05720 | i | 592, 0, 0 | Act_Identity | |
593 | | Input_Str_XRD_C_Ms | 0.00000 | -0.28110 | i | 593, 0, 0 | Act_Identity | |
594 | | Input_Str_XRD_C_Plg | 0.00000 | 0.15301 | i | 594, 0, 0 | Act_Identity | |
595 | | Input_Str_XRD_C_Plm | 0.00000 | 0.13329 | i | 595, 0, 0 | Act_Identity | |
596 | | Input_Str_XRD_C_Qz | 0.00000 | 0.21286 | i | 596, 0, 0 | Act_Identity | |
597 | | Input_Str_XRD_C_Rt | 0.00000 | -0.15306 | i | 597, 0, 0 | Act_Identity | |
598 | | Input_Str_XRD_C_Sme | 0.00000 | 0.03596 | i | 598, 0, 0 | Act_Identity | |
599 | | Input_Str_XRD_C_Tc | 0.00000 | -0.28286 | i | 599, 0, 0 | Act_Identity | |
600 | | Input_Str_XRD_C_Vrm | 0.00000 | 0.01300 | i | 600, 0, 0 | Act_Identity | |
601 | | Hidden_2_1 | 0.20765 | -1.68655 | h | 1, 2, 0 |||
602 | | Hidden_2_2 | 0.20939 | -1.27390 | h | 2, 2, 0 |||
603 | | Hidden_2_3 | 0.17573 | -1.64241 | h | 3, 2, 0 |||
604 | | Hidden_2_4 | 0.29427 | -0.84957 | h | 4, 2, 0 |||
605 | | Hidden_2_5 | 0.17668 | -1.47240 | h | 5, 2, 0 |||
606 | | Output_1 | 0.56644 | -0.63037 | o | 1, 4, 0 |||
----|----------|------------------------|----------|----------|----|-------------|--------------|----------|-------
connection definition section :
target | site | source:weight
-------|------|----------------------------------------------------------------------------------------------------------------
601 | | 600: 0.01113, 599:-0.16694, 598: 0.07455, 597:-0.25390, 596: 0.26497, 595: 0.05459, 594: 0.14804, 593:-0.18525,
592: 0.09752, 591: 0.00123, 590:-0.31415, 589: 0.28966, 588:-0.33958, 587:-0.29329, 586:-0.08468, 585: 0.20577,
584: 0.33467, 583:-0.21245, 582: 0.19591, 581: 0.02470, 580:-0.31672, 579:-0.18204, 578:-0.24825, 577:-0.12191,
576:-0.27648, 575:-0.19230, 574:-0.16589, 573: 0.07941, 572:-0.18542, 571: 0.06498, 570:-0.06939, 569: 0.31812,
568:-0.26382, 567:-0.13202, 566: 0.21160, 565: 0.00275, 564:-0.35661, 563:-0.08577, 562:-0.13918, 561:-0.20547,
560:-0.06378, 559:-0.02889, 558:-0.06169, 557:-0.21646, 556: 0.04009, 555:-0.28623, 554: 0.05998, 553: 0.21053,
552: 0.32320, 551:-0.16423, 550: 0.14411, 549:-0.23186, 548:-0.07519, 547:-0.27107, 546:-0.17791, 545:-0.11340,
544:-0.25414, 543: 0.05449, 542:-0.24530, 541:-0.16883, 540: 0.13200, 539:-0.28951, 538: 0.22911, 537:-0.03186,
536:-0.14073, 535:-0.04396, 534:-0.23093, 533: 0.23807, 532:-0.17091, 531: 0.30489, 530:-0.23378, 529:-0.10415,
528: 0.27740, 527:-0.09682, 526: 0.02363, 525: 0.02757, 524: 0.02695, 523:-0.03320, 522:-0.19887, 521: 0.12526,
520: 0.06008, 519: 0.00669, 518: 0.30625, 517: 0.40918, 516: 0.67020, 515: 0.01959, 514:-0.06351, 513: 0.47840,
512: 0.35672, 511: 0.28992, 510: 0.03991, 509: 0.01323, 508:-0.26203, 507: 0.13275, 506:-0.07764, 505: 0.09270,
504:-0.12162, 503: 0.30517, 502:-0.31641, 501:-0.02961, 500:-0.04719, 499: 0.09951, 498:-0.11299, 497:-0.18158,
496:-0.23042, 495:-0.24126, 494: 0.02634, 493: 0.01464, 492:-0.26601, 491:-0.03017, 490: 0.00729, 489:-0.09158,
488: 0.04997, 487:-0.21079, 486: 0.19490, 485:-0.39175, 484: 0.29823, 483: 0.02046, 482: 0.08220, 481: 0.18409,
480: 0.12493, 479: 0.29082, 478: 0.08730, 477:-0.11736, 476:-0.03013, 475: 0.09145, 474: 0.00616, 473:-0.27849,
472:-0.01710, 471:-0.01302, 470:-0.21792, 469:-0.16587, 468:-0.04696, 467:-0.05292, 466:-0.09001, 465:-0.19854,
464: 0.16328, 463: 0.30006, 462: 0.19521, 461: 0.11096, 460:-0.28098, 459:-0.25562, 458:-0.24756, 457:-0.30645,
456:-0.02375, 455: 0.11205, 454:-0.14126, 453:-0.25609, 452:-0.28816, 451: 0.25429, 450:-0.00806, 449:-0.21617,
448:-0.34358, 447: 0.00795, 446:-0.14975, 445:-0.32555, 444: 0.12676, 443:-0.28932, 442: 0.10651, 441:-0.33647,
440:-0.31515, 439: 0.09519, 438:-0.01351, 437: 0.09380, 436: 0.07243, 435:-0.13174, 434: 0.31319, 433:-0.29730,
432:-0.36471, 431:-0.17134, 430: 0.19588, 429: 0.23532, 428: 0.21139, 427:-0.17144, 426: 0.55630, 425:-0.30101,
424: 0.50986, 423: 0.04334, 422: 0.12744, 421: 0.06589, 420: 0.28379, 419:-0.27906, 418:-0.26694, 417:-0.15497,
416: 0.10806, 415: 0.38493, 414: 0.21213, 413: 0.04411, 412:-0.26718, 411: 0.31128, 410:-0.30556, 409: 0.13484,
408:-0.06702, 407: 0.14726, 406:-0.16917, 405:-0.12622, 404: 0.03011, 403: 0.21598, 402: 0.35494, 401: 0.07078,
400: 0.15666, 399: 0.26543, 398: 0.09867, 397:-0.10768, 396:-0.02516, 395: 0.34101, 394: 0.35473, 393:-0.16069,
392:-0.15058, 391:-0.06212, 390:-0.17102, 389: 0.14733, 388:-0.10583, 387: 0.16119, 386:-0.08222, 385: 0.29187,
384: 0.26291, 383: 0.02910, 382: 0.04344, 381:-0.14950, 380:-0.15623, 379: 0.19473, 378:-0.04747, 377:-0.31097,
376: 0.01781, 375: 0.17775, 374:-0.02589, 373: 0.28450, 372:-0.11830, 371:-0.01591, 370: 0.02650, 369: 0.14994,
368:-0.00476, 367: 0.24928, 366:-0.09552, 365: 0.06572, 364: 0.28794, 363:-0.23359, 362:-0.13036, 361:-0.10140,
360:-0.22687, 359:-0.39161, 358:-0.10565, 357: 0.31394, 356: 0.47666, 355:-0.13950, 354:-0.09256, 353: 0.27148,
352: 0.17557, 351:-0.16239, 350:-0.01794, 349:-0.19362, 348:-0.00304, 347:-0.04087, 346:-0.29580, 345: 0.24006,
344:-0.26033, 343: 0.27678, 342:-0.13708, 341:-0.27263, 340:-0.08721, 339:-0.07237, 338: 0.08320, 337:-0.22018,
336: 0.09305, 335:-0.11838, 334: 0.34445, 333:-0.06074, 332: 0.14409, 331:-0.26896, 330:-0.04743, 329:-0.01307,
328: 0.07566, 327:-0.00106, 326:-0.02742, 325:-0.01213, 324: 0.07087, 323: 0.07053, 322:-0.17283, 321: 0.16064,
320: 0.12896, 319: 0.06529, 318: 0.17590, 317:-0.25601, 316: 0.04134, 315: 0.31024, 314:-0.10117, 313:-0.01598,
312: 0.03569, 311: 0.01197, 310:-0.05876, 309:-0.07977, 308:-0.16423, 307:-0.26340, 306:-0.12540, 305:-0.20332,
304: 0.23249, 303: 0.30167, 302: 0.12640, 301: 0.29249, 300:-0.11582, 299: 0.14972, 298: 0.81155, 297:-0.09981,
296:-0.48513, 295:-0.11096, 294:-0.26394, 293: 0.39010, 292: 0.40373, 291:-0.32169, 290: 0.35689, 289: 0.17185,
288: 0.05340, 287:-0.21780, 286:-0.41250, 285: 0.17391, 284: 0.27308, 283:-0.28451, 282: 0.07929, 281: 0.36309,
280: 0.13487, 279:-0.19133, 278:-0.36502, 277:-0.26741, 276:-0.28242, 275:-0.15476, 274:-0.11367, 273:-0.22978,
272: 0.21169, 271: 0.14360, 270: 0.06899, 269:-0.27692, 268:-0.45357, 267: 0.16336, 266:-0.15999, 265: 0.13546,
264:-0.20251, 263: 0.07781, 262: 0.01015, 261: 0.17408, 260:-0.21057, 259:-0.04958, 258: 0.13554, 257: 0.29955,
256: 0.22157, 255:-0.46015, 254: 0.21500, 253:-0.17595, 252: 0.01829, 251:-0.01476, 250:-0.36313, 249:-0.17547,
248: 0.03735, 247: 0.10021, 246: 0.19502, 245: 0.17801, 244:-0.40689, 243: 0.18875, 242: 0.30731, 241: 0.26761,
240: 0.10291, 239: 0.31441, 238:-0.08119, 237: 0.09921, 236: 0.27916, 235: 0.19688, 234:-0.08132, 233:-0.45067,
232: 0.07412, 231: 0.12417, 230:-0.05768, 229: 0.09310, 228: 0.03452, 227: 0.18117, 226:-0.03701, 225: 0.29582,
224: 0.02994, 223:-0.15031, 222:-0.09217, 221:-0.02594, 220: 0.27788, 219: 0.03748, 218:-0.28055, 217: 0.11831,
216: 0.21940, 215: 0.05584, 214: 0.26488, 213:-0.19582, 212:-0.24238, 211:-0.17136, 210:-0.00372, 209:-0.03944,
208: 0.19277, 207: 0.03384, 206: 0.22831, 205:-0.10005, 204: 0.44503, 203: 0.18404, 202: 0.26228, 201: 0.14413,
200: 0.14909, 199:-0.10608, 198:-0.26207, 197:-0.15073, 196: 0.07597, 195: 0.18733, 194: 0.19448, 193: 0.26657,
192: 0.15994, 191: 0.00966, 190: 0.00681, 189:-0.13320, 188:-0.20661, 187: 0.22112, 186: 0.20743, 185: 0.14266,
184:-0.24331, 183:-0.16311, 182:-0.27337, 181:-0.31600, 180:-0.14054, 179: 0.17235, 178: 0.21943, 177:-0.15916,
176:-0.27448, 175:-0.19396, 174: 0.04982, 173:-0.27243, 172: 0.37251, 171: 0.22619, 170:-0.05652, 169: 0.13875,
168:-0.25339, 167: 0.54727, 166: 0.09832, 165:-0.20437, 164: 0.00157, 163: 0.29849, 162:-0.27783, 161:-0.10310,
160: 0.03494, 159:-0.15935, 158: 0.00155, 157:-0.04531, 156: 0.02202, 155:-0.08299, 154:-0.23947, 153:-0.18456,
152:-0.18134, 151:-0.11439, 150: 0.08598, 149:-0.11611, 148: 0.22331, 147:-0.12434, 146: 0.20027, 145: 0.06042,
144: 0.07246, 143: 0.17087, 142:-0.21337, 141:-0.40142, 140:-0.13057, 139: 0.04371, 138:-0.15187, 137:-0.02149,
136: 0.22049, 135: 0.08476, 134:-0.10746, 133:-0.26348, 132: 0.23322, 131: 0.21326, 130:-0.18932, 129:-0.15887,
128: 0.22749, 127: 0.12267, 126:-0.36850, 125: 0.03510, 124:-0.24361, 123:-0.22012, 122:-0.19351, 121: 0.22463,
120: 0.20703, 119: 0.02224, 118:-0.19413, 117: 0.19967, 116:-0.06878, 115: 0.22483, 114:-0.05224, 113: 0.32850,
112: 0.06703, 111:-0.27627, 110: 0.11825, 109:-0.14178, 108: 0.24122, 107:-0.17126, 106: 0.26395, 105: 0.27912,
104:-0.04843, 103:-0.07870, 102: 0.26508, 101:-0.01126, 100:-0.04770, 99: 0.22439, 98:-0.17490, 97:-0.08671,
96:-0.10403, 95: 0.03153, 94:-0.00451, 93:-0.05876, 92:-0.29394, 91: 0.23510, 90: 0.23440, 89:-0.08625,
88: 0.15811, 87:-0.22368, 86:-0.10244, 85:-0.30636, 84:-0.13431, 83:-0.13760, 82:-0.09368, 81: 0.34592,
80:-0.29292, 79: 0.04898, 78: 0.12840, 77:-0.13752, 76: 0.12610, 75: 0.24040, 74:-0.15546, 73:-0.19034,
72:-0.31521, 71: 0.27846, 70: 0.19863, 69: 0.14278, 68: 0.10096, 67:-0.21720, 66:-0.14810, 65:-0.00127,
64:-0.03053, 63:-0.06221, 62:-0.07923, 61: 0.21030, 60:-0.21321, 59:-0.28415, 58: 0.06433, 57: 0.47696,
56:-0.13348, 55: 0.17210, 54: 0.00829, 53:-1.02567, 52: 0.08326, 51: 0.27321, 50:-0.23955, 49: 0.07966,
48: 0.21641, 47:-0.15495, 46: 0.22723, 45: 0.18496, 44:-0.19546, 43:-0.03484, 42:-0.16759, 41:-0.30612,
40: 0.06104, 39: 0.04561, 38:-0.39570, 37: 0.16229, 36:-0.28626, 35:-0.04968, 34: 0.22855, 33: 0.06197,
32:-0.20617, 31: 0.13613, 30:-0.26357, 29:-0.19388, 28: 0.03895, 27:-0.28661, 26: 0.36939, 25: 0.17902,
24: 0.18722, 23: 0.20979, 22: 0.17044, 21: 0.19501, 20:-0.21736, 19:-0.01624, 18: 0.01264, 17:-0.14155,
16:-0.11040, 15: 0.11379, 14:-0.04552, 13:-0.09180, 12: 0.13289, 11:-0.29046, 10: 0.29461, 9:-0.28241,
8:-0.02823, 7: 0.21000, 6: 0.27532, 5:-0.26378, 4:-0.02591, 3: 0.12220, 2:-0.16112, 1: 0.28947
602 | | 600:-0.40451, 599: 0.01050, 598: 0.25698, 597:-0.01971, 596:-0.17477, 595:-0.20126, 594:-0.45937, 593: 0.28167,
592:-0.19447, 591: 0.06592, 590: 0.43203, 589:-0.21491, 588: 0.20401, 587: 0.05074, 586:-0.21856, 585:-0.04795,
584: 0.02135, 583: 0.01232, 582: 0.12038, 581: 0.20660, 580: 0.47073, 579: 0.22212, 578:-0.19287, 577: 0.01895,
576:-0.11074, 575:-0.14932, 574: 0.09196, 573:-0.06156, 572: 0.27276, 571: 0.23578, 570:-0.03939, 569:-0.09530,
568: 0.24505, 567: 0.15330, 566: 0.13660, 565: 0.09723, 564:-0.02341, 563: 0.06643, 562: 0.08273, 561:-0.28304,
560: 0.14881, 559:-0.02428, 558: 0.19513, 557: 0.02046, 556: 0.22013, 555:-0.15858, 554: 0.26735, 553: 0.44088,
552:-0.02608, 551: 0.47322, 550:-0.34776, 549: 0.15972, 548:-0.01831, 547: 0.01934, 546: 0.24862, 545: 0.37029,
544: 0.23569, 543:-0.13513, 542: 0.02128, 541: 0.19940, 540:-0.02402, 539:-0.14841, 538:-0.25813, 537: 0.59858,
536: 0.19027, 535: 0.06572, 534:-0.10372, 533:-0.00388, 532: 0.18997, 531: 0.18033, 530: 0.08122, 529:-0.24793,
528: 0.17724, 527:-0.27792, 526:-0.37973, 525: 0.09081, 524: 0.05802, 523: 0.24224, 522: 0.10472, 521: 0.21952,
520: 0.08164, 519:-0.11123, 518:-0.14859, 517:-0.10334, 516:-0.73129, 515: 0.22121, 514: 0.30850, 513:-0.25902,
512: 0.02832, 511:-0.16751, 510: 0.37611, 509: 0.08351, 508:-0.07675, 507: 0.21783, 506: 0.00879, 505: 0.03134,
504: 0.28042, 503:-0.20531, 502: 0.18326, 501: 0.07143, 500: 0.31378, 499:-0.08578, 498: 0.11781, 497: 0.15420,
496: 0.23465, 495:-0.06551, 494: 0.36900, 493: 0.17406, 492:-0.10399, 491: 0.38239, 490: 0.02739, 489: 0.19866,
488: 0.16292, 487:-0.03198, 486:-0.22434, 485:-0.06865, 484:-0.41293, 483:-0.23428, 482:-0.09922, 481: 0.20665,
480:-0.20714, 479: 0.01496, 478: 0.15106, 477:-0.15939, 476: 0.05210, 475: 0.12209, 474: 0.05026, 473: 0.47498,
472:-0.14150, 471: 0.24032, 470: 0.00884, 469: 0.22463, 468: 0.07883, 467: 0.09748, 466: 0.19099, 465:-0.21580,
464:-0.41012, 463:-0.13685, 462:-0.69130, 461:-0.49707, 460: 0.56708, 459: 0.37429, 458:-0.25788, 457: 0.43178,
456: 0.42703, 455:-0.00232, 454:-0.02868, 453:-0.19594, 452:-0.17331, 451: 0.11039, 450:-0.40812, 449:-0.36457,
448: 0.28817, 447: 0.12077, 446: 0.13750, 445: 0.24044, 444: 0.30684, 443:-0.01591, 442:-0.00832, 441: 0.04607,
440: 0.04799, 439:-0.13392, 438: 0.08795, 437: 0.06481, 436: 0.60142, 435:-0.14371, 434:-0.32561, 433: 0.24357,
432:-0.14346, 431:-0.21885, 430:-0.13101, 429:-0.17018, 428:-0.01221, 427:-0.33354, 426:-0.60936, 425: 0.22769,
424: 0.31681, 423:-0.31179, 422:-0.55005, 421:-0.03946, 420:-0.27889, 419: 0.04145, 418: 0.04599, 417: 0.01750,
416:-0.14103, 415:-0.05740, 414:-0.19449, 413: 0.52515, 412:-0.16010, 411:-0.42980, 410: 0.09247, 409: 0.58800,
408: 0.07464, 407:-0.09567, 406: 0.05155, 405: 0.08525, 404: 0.31657, 403:-0.26388, 402:-0.07609, 401:-0.18270,
400: 0.34175, 399: 0.08498, 398:-0.62183, 397:-0.25209, 396: 0.38168, 395:-0.08947, 394: 0.09720, 393: 0.06730,
392: 0.04781, 391: 0.30068, 390: 0.11618, 389: 0.15618, 388: 0.09567, 387:-0.32079, 386: 0.08535, 385:-0.24722,
384:-0.23188, 383: 0.06894, 382:-0.12765, 381: 0.20214, 380: 0.23969, 379:-0.19352, 378: 0.15453, 377: 0.00290,
376:-0.07582, 375:-0.02260, 374: 0.22083, 373: 0.16790, 372: 0.11297, 371: 0.07158, 370: 0.02773, 369: 0.21119,
368:-0.19297, 367:-0.28593, 366:-0.05009, 365:-0.05420, 364:-0.08704, 363: 0.18430, 362: 0.37341, 361: 0.09752,
360:-0.08965, 359: 0.69416, 358: 0.19057, 357:-0.42802, 356:-0.51148, 355: 0.26894, 354:-0.06806, 353: 0.03760,
352:-0.00189, 351:-0.11622, 350: 0.22265, 349: 0.21677, 348: 0.08530, 347:-0.18921, 346: 0.23953, 345:-0.06465,
344: 0.33446, 343:-0.13517, 342: 0.25181, 341:-0.04604, 340:-0.19718, 339: 0.17936, 338: 0.31543, 337: 0.38938,
336:-0.40635, 335:-0.02939, 334:-0.24760, 333: 0.30883, 332: 0.19909, 331: 0.02177, 330:-0.29204, 329:-0.00186,
328:-0.08329, 327: 0.01400, 326: 0.10005, 325: 0.27287, 324:-0.27875, 323:-0.23160, 322:-0.25191, 321: 0.01849,
320: 0.06810, 319:-0.01950, 318:-0.00806, 317: 0.26611, 316:-0.06583, 315:-0.40927, 314: 0.60721, 313: 0.34219,
312: 0.18865, 311:-0.04946, 310:-0.41295, 309:-0.26030, 308:-0.02737, 307:-0.17423, 306:-0.09516, 305:-0.38917,
304: 0.13932, 303:-0.09758, 302: 0.26984, 301:-0.18968, 300:-0.08704, 299:-0.01068, 298: 0.00394, 297:-0.13001,
296: 0.67293, 295:-0.24800, 294:-0.21391, 293:-0.77770, 292:-0.51783, 291: 0.21522, 290:-0.65233, 289:-0.21667,
288:-0.46558, 287:-0.01197, 286: 0.53903, 285:-0.25380, 284:-0.11721, 283:-0.19870, 282: 0.14871, 281:-0.11580,
280:-0.27303, 279:-0.09995, 278: 0.50543, 277:-0.01916, 276: 0.17032, 275: 0.11313, 274:-0.34437, 273: 0.02720,
272:-0.29385, 271:-0.07518, 270: 0.01602, 269:-0.21707, 268: 0.18473, 267: 0.13134, 266:-0.27260, 265:-0.19629,
264:-0.11563, 263: 0.05346, 262: 0.01013, 261: 0.13198, 260:-0.39109, 259:-0.23883, 258: 0.58870, 257:-0.12652,
256:-0.59413, 255: 0.68115, 254:-0.21204, 253: 0.00551, 252: 0.02495, 251:-0.18744, 250: 0.02555, 249: 0.25670,
248:-0.60826, 247:-0.28940, 246:-0.30386, 245: 0.06453, 244:-0.11850, 243:-0.21698, 242:-0.76734, 241:-1.24201,
240:-0.06690, 239:-0.45256, 238:-0.19740, 237: 0.12666, 236: 0.24608, 235: 0.36658, 234:-0.26970, 233: 0.31875,
232:-0.13514, 231:-0.77361, 230: 0.01953, 229: 0.00672, 228: 0.21156, 227:-0.24087, 226: 0.17751, 225:-0.22605,
224: 0.17016, 223: 0.11339, 222: 0.44237, 221: 0.15435, 220:-0.55723, 219:-0.24768, 218:-0.20055, 217:-0.08340,
216: 0.09574, 215:-0.06877, 214:-0.01774, 213:-0.21023, 212: 0.18737, 211:-0.18103, 210: 0.28017, 209:-0.24858,
208: 0.24410, 207: 0.07135, 206:-0.01594, 205: 0.08561, 204:-0.30148, 203:-0.10412, 202: 0.19811, 201: 0.28056,
200: 0.20556, 199: 0.26884, 198: 0.10258, 197: 0.19553, 196:-0.24923, 195: 0.25288, 194:-0.04621, 193: 0.26471,
192:-0.15749, 191: 0.17970, 190: 0.19051, 189: 0.29429, 188:-0.14844, 187:-0.41714, 186:-0.29101, 185: 0.03564,
184:-0.00063, 183:-0.26948, 182:-0.11025, 181: 0.39444, 180:-0.01630, 179: 0.06955, 178: 0.13669, 177:-0.01924,
176:-0.37773, 175:-0.10698, 174: 0.08894, 173: 0.05774, 172: 0.10715, 171:-0.34304, 170:-0.29214, 169: 0.19289,
168: 0.25010, 167:-0.78915, 166: 0.06359, 165: 0.07734, 164:-0.28473, 163:-0.24924, 162: 0.24338, 161: 0.02730,
160:-0.24564, 159:-0.21202, 158:-0.18649, 157: 0.20179, 156: 0.34606, 155:-0.00561, 154: 0.04825, 153: 0.20237,
152: 0.27509, 151: 0.18192, 150:-0.28308, 149:-0.09290, 148:-0.25986, 147:-0.01502, 146:-0.25284, 145: 0.06696,
144:-0.11894, 143:-0.32491, 142: 0.02519, 141:-0.23695, 140:-0.47714, 139:-0.18847, 138: 0.28256, 137:-0.00454,
136: 0.28915, 135:-0.04748, 134:-0.27333, 133:-0.06651, 132:-0.14920, 131: 0.12231, 130:-0.22406, 129:-0.36908,
128: 0.38946, 127: 0.17512, 126: 0.78437, 125:-0.18086, 124: 0.31788, 123: 0.07317, 122: 0.15150, 121: 0.09347,
120: 0.07667, 119: 0.22589, 118: 0.18211, 117: 0.26468, 116: 0.19884, 115:-0.24360, 114: 0.28399, 113:-0.28322,
112:-0.18558, 111: 0.25550, 110: 0.03134, 109:-0.33869, 108: 0.13029, 107: 0.05015, 106:-0.19624, 105: 0.05679,
104: 0.17401, 103: 0.03657, 102:-0.11230, 101:-0.29940, 100: 0.44462, 99:-0.05140, 98: 0.10479, 97:-0.22174,
96:-0.07872, 95:-0.47979, 94: 0.19587, 93:-0.53529, 92: 0.44065, 91: 0.19027, 90:-0.14778, 89:-0.13135,
88: 0.09126, 87: 0.32729, 86: 0.02026, 85:-0.03774, 84:-0.04430, 83:-0.01930, 82:-0.03157, 81:-0.45708,
80: 0.22014, 79: 0.50022, 78:-0.03620, 77:-0.28786, 76:-0.28591, 75: 0.00681, 74: 0.09079, 73:-0.06798,
72: 0.42184, 71:-0.36488, 70:-0.22580, 69: 0.01923, 68: 0.17692, 67: 0.23496, 66: 0.21558, 65:-0.22103,
64:-0.01921, 63: 0.40915, 62: 0.44356, 61:-0.34965, 60: 0.20015, 59: 0.01967, 58: 0.08115, 57:-0.43038,
56:-0.14731, 55: 0.30834, 54:-0.16837, 53: 1.67721, 52: 0.12212, 51: 0.01365, 50: 0.04612, 49: 0.68061,
48:-0.34494, 47:-0.26541, 46: 0.24129, 45: 0.15752, 44:-0.14276, 43:-0.15643, 42:-0.01458, 41: 0.34536,
40: 0.27999, 39:-0.07761, 38: 0.18133, 37:-0.25250, 36:-0.08825, 35:-0.00245, 34: 0.06167, 33: 0.29581,
32: 0.22277, 31:-0.15134, 30:-0.20759, 29: 0.15191, 28:-0.19313, 27: 0.24754, 26:-0.14901, 25:-0.30962,
24: 0.05922, 23: 0.05707, 22: 0.09727, 21:-0.02486, 20:-0.21034, 19:-0.15563, 18: 0.07045, 17: 0.00760,
16: 0.38407, 15:-0.24535, 14: 0.27101, 13:-0.22450, 12:-0.14445, 11: 0.49783, 10:-0.38172, 9: 0.25411,
8:-0.16554, 7:-0.05597, 6: 0.15872, 5:-0.31868, 4:-0.02981, 3:-0.31219, 2:-0.37469, 1: 0.01920
603 | | 600:-0.20863, 599: 0.21378, 598:-0.23093, 597: 0.21673, 596: 0.09911, 595:-0.26871, 594:-0.19775, 593: 0.20607,
592:-0.10191, 591:-0.06818, 590: 0.13995, 589:-0.19603, 588:-0.11377, 587:-0.15540, 586: 0.19511, 585:-0.16100,
584:-0.08435, 583:-0.05523, 582: 0.19449, 581:-0.23351, 580: 0.10176, 579:-0.28312, 578: 0.11664, 577: 0.09937,
576:-0.21662, 575:-0.05099, 574: 0.03489, 573: 0.01650, 572:-0.24430, 571:-0.22789, 570: 0.27464, 569:-0.17616,
568:-0.28997, 567:-0.03240, 566:-0.20683, 565: 0.19439, 564:-0.24143, 563: 0.15646, 562: 0.02187, 561:-0.29206,
560: 0.06151, 559: 0.36583, 558: 0.02047, 557: 0.10910, 556:-0.11875, 555: 0.20465, 554: 0.23459, 553: 0.07644,
552: 0.03374, 551: 0.00109, 550: 0.33390, 549:-0.19293, 548: 0.21521, 547: 0.05782, 546:-0.26838, 545: 0.12150,
544:-0.27263, 543: 0.27470, 542:-0.14995, 541: 0.28960, 540: 0.17197, 539:-0.06710, 538: 0.00891, 537:-0.26726,
536: 0.17134, 535: 0.19013, 534:-0.13199, 533:-0.29975, 532: 0.24250, 531:-0.21624, 530: 0.26780, 529:-0.03892,
528: 0.00309, 527:-0.11610, 526:-0.10088, 525:-0.21701, 524:-0.02208, 523: 0.05400, 522:-0.20179, 521: 0.24025,
520: 0.20974, 519: 0.20573, 518:-0.09273, 517:-0.23229, 516:-0.00859, 515:-0.12810, 514:-0.06859, 513: 0.00884,
512:-0.16754, 511:-0.09939, 510:-0.35276, 509: 0.29452, 508:-0.07783, 507:-0.25329, 506: 0.02727, 505:-0.00998,
504:-0.02694, 503: 0.18806, 502:-0.27279, 501:-0.37833, 500: 0.20332, 499:-0.15005, 498:-0.27051, 497:-0.18027,
496:-0.16270, 495:-0.09128, 494:-0.11059, 493: 0.06209, 492:-0.20942, 491:-0.08068, 490:-0.27520, 489: 0.00060,
488:-0.20044, 487: 0.01225, 486: 0.20669, 485:-0.29590, 484:-0.01744, 483: 0.30907, 482: 0.05278, 481:-0.09171,
480:-0.23205, 479: 0.03843, 478:-0.33086, 477:-0.07365, 476: 0.37380, 475:-0.08989, 474: 0.29464, 473:-0.37761,
472: 0.18537, 471:-0.38792, 470: 0.04186, 469: 0.00522, 468:-0.09907, 467:-0.01883, 466: 0.02589, 465: 0.33839,
464: 0.00543, 463: 0.27574, 462: 0.46272, 461: 0.13851, 460: 0.11377, 459:-0.13997, 458: 0.11709, 457:-0.43441,
456: 0.13791, 455:-0.33149, 454:-0.28856, 453:-0.22189, 452:-0.17180, 451:-0.19261, 450: 0.00697, 449:-0.08197,
448: 0.02652, 447:-0.10070, 446: 0.02899, 445:-0.27894, 444:-0.19281, 443: 0.08897, 442: 0.01396, 441:-0.14022,
440: 0.23809, 439:-0.04453, 438: 0.01241, 437:-0.09519, 436:-0.27997, 435:-0.05602, 434:-0.05223, 433:-0.04331,
432:-0.08780, 431: 0.33186, 430:-0.00154, 429: 0.32400, 428:-0.33577, 427:-0.16210, 426:-0.28356, 425: 0.25338,
424: 0.15790, 423:-0.11401, 422: 0.28409, 421: 0.07669, 420: 0.14995, 419: 0.27064, 418: 0.18014, 417:-0.13253,
416:-0.16355, 415:-0.10439, 414:-0.16438, 413:-0.19546, 412:-0.25404, 411: 0.12275, 410: 0.14168, 409:-0.15136,
408: 0.16833, 407:-0.08162, 406: 0.14750, 405:-0.14781, 404:-0.00125, 403: 0.00840, 402: 0.00728, 401: 0.00677,
400: 0.07186, 399:-0.05823, 398: 0.54673, 397: 0.07889, 396:-0.23156, 395:-0.07470, 394: 0.23562, 393:-0.16593,
392: 0.02575, 391:-0.10701, 390:-0.09124, 389:-0.01944, 388:-0.20434, 387:-0.09416, 386: 0.02692, 385: 0.26780,
384: 0.08967, 383: 0.08114, 382: 0.09929, 381: 0.23883, 380: 0.06969, 379: 0.00193, 378:-0.03631, 377:-0.25366,
376:-0.19266, 375:-0.00258, 374: 0.06657, 373: 0.18956, 372: 0.17706, 371: 0.16717, 370: 0.25180, 369:-0.18975,
368:-0.06887, 367: 0.17419, 366: 0.08929, 365: 0.16932, 364:-0.18085, 363:-0.11645, 362: 0.12901, 361:-0.34281,
360: 0.17435, 359:-0.12665, 358: 0.01604, 357: 0.11487, 356: 0.58856, 355:-0.14565, 354: 0.15210, 353: 0.20260,
352: 0.26357, 351:-0.02617, 350:-0.05234, 349: 0.12552, 348: 0.04590, 347:-0.16194, 346:-0.17893, 345: 0.11705,
344: 0.06647, 343: 0.00155, 342:-0.00368, 341:-0.28995, 340: 0.09358, 339:-0.30992, 338:-0.36591, 337:-0.21622,
336: 0.22750, 335:-0.12885, 334: 0.28133, 333:-0.27186, 332:-0.28891, 331:-0.12516, 330: 0.14913, 329: 0.15403,
328: 0.12332, 327:-0.08229, 326:-0.03071, 325: 0.21841, 324: 0.11171, 323: 0.20090, 322:-0.17550, 321:-0.16824,
320:-0.14849, 319:-0.16909, 318:-0.19431, 317: 0.23184, 316:-0.07088, 315: 0.12364, 314:-0.10671, 313: 0.02758,
312:-0.11704, 311: 0.22943, 310:-0.11750, 309: 0.09617, 308: 0.23214, 307:-0.06002, 306: 0.15585, 305:-0.23159,
304:-0.14860, 303: 0.23668, 302:-0.05769, 301: 0.03517, 300:-0.03959, 299:-0.45173, 298: 0.09837, 297:-0.05461,
296:-0.23313, 295:-0.22827, 294:-0.17858, 293: 0.18476, 292: 0.28799, 291: 0.18116, 290: 0.02273, 289: 0.13358,
288:-0.06046, 287:-0.36231, 286:-0.37641, 285: 0.04177, 284: 0.27008, 283:-0.10298, 282:-0.30044, 281: 0.35878,
280: 0.22123, 279:-0.05174, 278: 0.16959, 277: 0.10452, 276:-0.09863, 275:-0.26093, 274:-0.05651, 273: 0.01602,
272: 0.05049, 271: 0.11411, 270: 0.25422, 269: 0.16356, 268: 0.13075, 267:-0.03363, 266: 0.03958, 265:-0.11467,
264: 0.05600, 263: 0.22883, 262:-0.01072, 261: 0.26288, 260: 0.07868, 259:-0.21332, 258:-0.10193, 257: 0.29656,
256: 0.17439, 255:-0.21409, 254: 0.20659, 253:-0.19983, 252: 0.35656, 251: 0.26275, 250:-0.27628, 249: 0.28773,
248: 0.29305, 247: 0.08517, 246: 0.19474, 245: 0.27349, 244: 0.05656, 243: 0.09195, 242: 0.35914, 241: 0.21096,
240:-0.29279, 239:-0.13804, 238: 0.10764, 237: 0.23172, 236:-0.09779, 235: 0.07648, 234:-0.29596, 233:-0.06233,
232:-0.22745, 231: 0.13662, 230:-0.30759, 229: 0.10163, 228: 0.00896, 227:-0.08036, 226: 0.13468, 225: 0.10444,
224:-0.24793, 223:-0.19311, 222:-0.09564, 221:-0.10130, 220: 0.10627, 219:-0.22313, 218: 0.08290, 217:-0.16188,
216:-0.02102, 215:-0.25893, 214:-0.19708, 213: 0.18124, 212: 0.00037, 211: 0.12203, 210:-0.03398, 209:-0.04519,
208: 0.12957, 207:-0.09505, 206:-0.31094, 205: 0.04991, 204:-0.14912, 203:-0.06113, 202: 0.13576, 201: 0.25802,
200:-0.13927, 199: 0.13143, 198:-0.01527, 197:-0.19488, 196:-0.02541, 195:-0.02891, 194:-0.13119, 193:-0.19623,
192:-0.29029, 191:-0.33738, 190: 0.20518, 189:-0.06521, 188:-0.29694, 187: 0.05170, 186:-0.00081, 185: 0.29350,
184:-0.11992, 183: 0.25001, 182:-0.14311, 181:-0.25407, 180: 0.03451, 179: 0.30330, 178: 0.21769, 177:-0.22518,
176: 0.02799, 175:-0.13786, 174: 0.13280, 173: 0.05536, 172:-0.20266, 171: 0.02415, 170: 0.01548, 169:-0.00979,
168:-0.10867, 167: 0.04309, 166: 0.01421, 165: 0.11882, 164:-0.26493, 163: 0.04993, 162: 0.05800, 161: 0.01427,
160:-0.23042, 159: 0.21461, 158:-0.01752, 157:-0.17920, 156: 0.22456, 155: 0.14923, 154:-0.32063, 153: 0.12642,
152: 0.21377, 151: 0.14002, 150: 0.11776, 149:-0.24012, 148:-0.24994, 147: 0.31644, 146:-0.13206, 145: 0.13339,
144:-0.30623, 143: 0.29582, 142: 0.03350, 141:-0.15473, 140:-0.07465, 139:-0.18858, 138:-0.26255, 137:-0.28617,
136: 0.23517, 135:-0.01486, 134: 0.30560, 133: 0.25209, 132: 0.08773, 131: 0.27368, 130: 0.28540, 129: 0.22422,
128:-0.00728, 127:-0.11940, 126:-0.06928, 125:-0.04596, 124: 0.01836, 123: 0.10387, 122: 0.22275, 121: 0.17003,
120:-0.23860, 119:-0.10957, 118:-0.27931, 117:-0.19768, 116: 0.13116, 115: 0.11891, 114:-0.00978, 113:-0.24387,
112:-0.10972, 111:-0.00942, 110:-0.26813, 109:-0.16770, 108: 0.14861, 107:-0.27550, 106: 0.15410, 105:-0.28269,
104: 0.18714, 103: 0.28639, 102: 0.15770, 101: 0.25691, 100:-0.20080, 99:-0.21853, 98:-0.23197, 97: 0.05765,
96:-0.22551, 95:-0.07183, 94: 0.07209, 93:-0.14176, 92:-0.32462, 91:-0.03817, 90:-0.00702, 89: 0.23842,
88: 0.11771, 87:-0.23915, 86: 0.24832, 85: 0.23100, 84:-0.22435, 83:-0.33725, 82:-0.03913, 81: 0.03417,
80: 0.07195, 79:-0.03718, 78:-0.19284, 77: 0.03200, 76:-0.05988, 75: 0.27732, 74:-0.14343, 73: 0.09370,
72:-0.06791, 71:-0.14544, 70: 0.19328, 69:-0.28306, 68:-0.10919, 67:-0.10714, 66:-0.15379, 65: 0.30097,
64: 0.12482, 63:-0.25009, 62: 0.02204, 61: 0.37830, 60:-0.11041, 59:-0.07392, 58: 0.19811, 57:-0.03583,
56: 0.28940, 55: 0.04501, 54: 0.21000, 53:-0.78318, 52:-0.20027, 51:-0.28176, 50:-0.17073, 49:-0.21895,
48:-0.23690, 47: 0.06410, 46: 0.18246, 45:-0.10641, 44:-0.17759, 43: 0.19696, 42:-0.02115, 41:-0.26963,
40:-0.02475, 39: 0.03391, 38:-0.07740, 37:-0.13899, 36: 0.23175, 35:-0.23327, 34:-0.30496, 33:-0.10238,
32: 0.10242, 31: 0.19624, 30: 0.25391, 29:-0.00805, 28:-0.25946, 27: 0.08612, 26:-0.11903, 25: 0.03402,
24:-0.35241, 23:-0.13219, 22: 0.08578, 21:-0.03502, 20:-0.24982, 19:-0.26922, 18: 0.05535, 17: 0.06093,
16:-0.07677, 15:-0.10522, 14: 0.18509, 13:-0.19810, 12:-0.29430, 11:-0.19250, 10: 0.00239, 9:-0.18816,
8:-0.17389, 7:-0.11127, 6:-0.03991, 5:-0.15409, 4: 0.19237, 3:-0.11094, 2:-0.02868, 1: 0.10692
604 | | 600: 0.43595, 599:-0.00454, 598: 0.01227, 597:-0.19505, 596:-0.17983, 595:-0.23581, 594: 0.47385, 593: 0.00555,
592: 0.10079, 591:-0.54115, 590:-0.01275, 589: 0.04598, 588:-0.48818, 587:-0.17583, 586:-0.00645, 585:-0.12591,
584:-0.00567, 583: 0.34518, 582: 0.29101, 581:-0.96583, 580:-0.46490, 579:-0.34448, 578: 0.00628, 577:-0.01532,
576:-0.27284, 575: 0.05076, 574: 0.03997, 573:-0.38792, 572:-0.22697, 571: 0.10862, 570: 1.00784, 569: 0.01815,
568:-0.26435, 567: 0.32154, 566:-0.47171, 565: 0.35652, 564:-0.36119, 563:-0.06158, 562:-0.09843, 561:-0.11419,
560: 0.04102, 559:-0.09674, 558:-0.16601, 557:-0.95275, 556:-0.23898, 555: 0.22697, 554: 0.09989, 553:-0.12859,
552: 0.07510, 551:-0.82833, 550: 0.07138, 549:-0.16004, 548: 0.32620, 547: 0.39469, 546:-0.99661, 545:-0.59935,
544:-0.28342, 543:-0.02321, 542:-0.32760, 541: 0.18615, 540:-0.05471, 539:-0.30666, 538:-0.25846, 537:-0.57771,
536:-0.58749, 535: 0.07737, 534: 0.13604, 533: 0.21245, 532: 0.10074, 531: 0.01300, 530: 0.54468, 529: 0.61931,
528: 0.17351, 527:-0.08536, 526: 0.09407, 525: 0.05287, 524: 0.08295, 523: 0.00649, 522:-0.02995, 521:-0.15735,
520: 0.08633, 519:-0.08804, 518: 0.01625, 517: 0.00327, 516: 0.39748, 515:-0.15245, 514:-0.45754, 513:-0.42052,
512:-0.58627, 511:-0.12152, 510:-0.64693, 509:-0.01627, 508:-0.14595, 507:-0.16911, 506: 0.06844, 505:-0.01834,
504:-0.26572, 503: 0.45587, 502:-0.40755, 501:-0.33350, 500:-0.13922, 499:-0.29933, 498:-0.08269, 497: 0.22938,
496:-0.08301, 495: 0.06593, 494: 0.10270, 493:-0.35721, 492:-0.26222, 491:-0.27249, 490:-0.51737, 489:-0.22576,
488:-0.24580, 487: 0.34616, 486:-0.09028, 485:-0.70395, 484: 0.44880, 483:-0.03095, 482:-0.14913, 481:-0.28748,
480:-0.00288, 479: 0.13632, 478:-0.22211, 477:-0.01899, 476: 0.21287, 475: 0.22223, 474:-0.01871, 473:-0.36065,
472: 0.17290, 471:-0.50057, 470: 0.23353, 469:-0.69688, 468: 0.56591, 467: 0.27631, 466:-0.52089, 465: 0.38836,
464:-0.05611, 463: 0.14367, 462: 0.35953, 461: 0.73990, 460:-0.41294, 459:-0.17388, 458: 0.44442, 457:-0.78351,
456:-0.50180, 455:-0.35042, 454:-0.07996, 453:-0.27515, 452: 0.08810, 451: 0.16796, 450: 0.74819, 449: 0.72753,
448:-0.37647, 447:-0.60993, 446: 0.14913, 445:-0.52746, 444:-0.52703, 443:-0.47273, 442:-0.17902, 441: 0.14989,
440: 0.53993, 439: 0.05866, 438: 0.48794, 437:-0.07063, 436:-0.31200, 435: 0.00170, 434: 0.53762, 433:-0.41611,
432:-0.21965, 431: 0.30368, 430:-0.45414, 429: 0.59889, 428: 0.01460, 427:-0.19110, 426:-1.89629, 425:-0.36979,
424: 0.09048, 423:-0.30884, 422: 0.23003, 421:-0.00189, 420: 0.27589, 419:-0.13415, 418:-0.28782, 417:-0.08509,
416:-0.19342, 415: 0.39726, 414:-0.10359, 413:-0.53806, 412: 0.20724, 411: 0.70722, 410:-0.16961, 409:-0.48183,
408: 0.01205, 407:-0.28626, 406:-0.30869, 405: 0.15449, 404: 0.11080, 403: 0.60247, 402: 0.02494, 401: 0.08973,
400:-0.23208, 399:-0.10341, 398: 0.67811, 397: 0.59650, 396:-0.78408, 395: 0.88021, 394: 0.06971, 393:-0.25285,
392: 0.09683, 391:-0.04159, 390:-0.16989, 389:-0.26599, 388: 0.21489, 387:-0.16797, 386: 0.48327, 385: 0.23208,
384: 0.33834, 383:-0.20954, 382: 0.60749, 381:-0.10159, 380:-0.31056, 379:-0.15848, 378: 0.07103, 377:-0.35811,
376:-0.01389, 375:-0.22998, 374: 0.00876, 373: 0.54452, 372:-0.13582, 371:-0.28154, 370:-0.31574, 369:-0.24928,
368: 0.02945, 367: 0.43039, 366:-0.39615, 365:-0.11236, 364: 0.02123, 363:-0.17480, 362:-0.40426, 361:-0.22781,
360: 0.21146, 359:-1.22897, 358: 0.14604, 357: 0.34836, 356: 1.24333, 355:-0.27716, 354: 0.14258, 353:-0.15329,
352:-0.18950, 351:-0.26401, 350: 0.27107, 349:-0.19462, 348:-0.42418, 347: 0.39947, 346:-0.18091, 345: 0.52284,
344:-0.12592, 343:-0.12245, 342:-0.27280, 341: 0.18457, 340: 0.03986, 339:-0.97842, 338:-0.11840, 337:-0.44413,
336: 0.43415, 335:-0.41769, 334: 0.39241, 333:-0.25825, 332: 0.18839, 331: 0.26569, 330:-0.17102, 329:-0.12434,
328: 0.12550, 327:-0.23459, 326: 0.29786, 325: 0.05287, 324:-0.24008, 323:-0.00448, 322:-0.03892, 321:-0.15674,
320: 0.17040, 319:-0.14600, 318:-0.18918, 317: 0.20109, 316:-1.04211, 315: 0.33242, 314:-1.14152, 313: 0.03567,
312:-0.55265, 311: 0.27965, 310: 0.15824, 309:-0.00478, 308: 0.13780, 307:-0.25872, 306: 0.47049, 305: 0.13044,
304: 0.44467, 303: 0.37339, 302:-0.33675, 301:-0.29191, 300:-0.28594, 299:-0.15594, 298:-0.33153, 297: 0.26523,
296:-1.15277, 295: 0.50823, 294: 0.28218, 293: 1.41441, 292: 0.65745, 291:-0.31525, 290: 0.42700, 289: 0.03780,
288: 0.74428, 287:-0.45667, 286:-0.66920, 285: 0.31086, 284: 0.40980, 283:-0.31339, 282:-0.30482, 281: 0.52384,
280: 0.14185, 279: 0.05895, 278:-0.45645, 277: 0.00071, 276:-0.25286, 275:-1.20204, 274: 0.62521, 273:-0.14530,
272: 0.26749, 271: 0.25944, 270: 0.21723, 269:-0.24586, 268:-0.27392, 267:-0.48475, 266: 0.32810, 265:-0.13632,
264: 0.17568, 263: 0.09733, 262:-0.10474, 261:-0.14175, 260: 0.59269, 259:-0.03744, 258:-0.95238, 257: 0.14220,
256: 0.67726, 255:-1.01409, 254: 0.21742, 253: 0.48818, 252: 0.39539, 251:-0.03681, 250:-0.43924, 249: 0.16496,
248: 0.96784, 247: 0.17161, 246: 0.29594, 245:-0.02426, 244:-0.48532, 243:-0.01695, 242: 0.98089, 241: 2.48999,
240: 0.13084, 239: 0.45191, 238: 0.27527, 237:-0.19954, 236: 0.05901, 235: 0.01161, 234: 0.19267, 233:-1.29326,
232: 0.28281, 231: 1.36233, 230:-0.27785, 229: 0.17087, 228:-0.76602, 227:-0.04152, 226: 0.04193, 225: 0.33866,
224:-0.09438, 223: 0.13379, 222:-0.04507, 221: 0.05018, 220: 0.62162, 219:-0.06264, 218: 0.07961, 217: 0.06025,
216:-0.05015, 215:-0.07882, 214:-0.27092, 213:-0.16479, 212: 0.14959, 211:-0.14565, 210:-0.00321, 209:-0.05453,
208:-0.17698, 207:-0.73942, 206:-0.35141, 205: 0.09777, 204: 0.45179, 203: 0.45632, 202:-0.01184, 201:-0.17238,
200: 0.12767, 199:-0.29250, 198: 0.28978, 197:-0.08040, 196:-0.28179, 195:-0.09738, 194: 0.10811, 193: 0.02707,
192:-0.22834, 191:-0.40177, 190: 0.16711, 189:-0.23378, 188: 0.01227, 187: 0.40694, 186: 0.34851, 185:-0.20632,
184:-0.50394, 183: 0.38312, 182:-0.03649, 181:-0.51842, 180: 0.29892, 179:-0.13469, 178:-0.24135, 177:-0.28476,
176: 0.47302, 175:-0.32144, 174:-0.22917, 173:-0.08915, 172: 0.09319, 171:-0.39928, 170: 0.45033, 169: 0.16842,
168:-0.17402, 167: 0.49343, 166:-0.70895, 165:-0.39885, 164:-0.52635, 163: 0.07194, 162:-0.08493, 161: 0.15803,
160:-0.27394, 159: 0.15310, 158:-0.05323, 157:-0.38160, 156:-0.19141, 155:-0.63489, 154:-0.15156, 153:-0.02622,
152:-0.05302, 151:-0.28982, 150:-0.24595, 149: 0.20919, 148:-0.20775, 147: 0.43164, 146: 0.31484, 145:-0.38205,
144: 0.15922, 143: 0.37402, 142: 0.26265, 141:-0.91392, 140: 0.20788, 139: 0.26162, 138: 0.25424, 137:-0.02190,
136: 0.00365, 135:-0.26357, 134:-0.06359, 133:-0.01645, 132:-0.12152, 131: 0.15408, 130: 0.47015, 129: 0.07045,
128:-0.07874, 127: 0.02497, 126:-1.19543, 125: 0.63187, 124:-0.33465, 123: 0.18242, 122: 0.25584, 121: 0.03028,
120:-0.18015, 119:-0.02908, 118:-0.60898, 117: 0.14611, 116:-0.45737, 115: 0.40402, 114:-0.39863, 113: 0.41345,
112: 0.44189, 111:-0.57989, 110:-0.01825, 109:-0.13462, 108:-0.06644, 107:-0.20147, 106:-0.27157, 105: 0.09221,
104: 0.02906, 103: 0.05084, 102:-0.21476, 101:-0.00323, 100:-0.64107, 99: 0.45427, 98:-0.21581, 97: 0.09538,
96:-0.18076, 95: 0.23136, 94:-0.86694, 93: 0.62522, 92:-0.69128, 91: 0.19252, 90: 0.20722, 89: 0.01321,
88: 0.20736, 87:-0.95666, 86: 0.50788, 85:-0.45904, 84: 0.14993, 83: 0.07771, 82: 0.24850, 81: 0.51666,
80:-0.14116, 79:-0.71664, 78: 0.40200, 77: 0.28557, 76: 0.31152, 75: 0.02346, 74:-0.21334, 73: 0.26264,
72:-0.43136, 71: 0.45390, 70:-0.29221, 69: 0.32624, 68:-0.25167, 67: 0.18729, 66: 0.00485, 65: 0.86559,
64:-0.02324, 63:-0.31090, 62:-0.23006, 61: 0.96468, 60:-0.09346, 59:-0.16952, 58:-0.15155, 57: 0.84033,
56: 0.62627, 55:-0.50518, 54: 0.37338, 53:-4.23898, 52: 0.09926, 51:-0.49634, 50:-0.03217, 49:-0.54935,
48: 0.51720, 47:-0.13281, 46: 0.19723, 45: 0.06264, 44:-0.05335, 43: 0.35171, 42:-0.34330, 41:-0.48108,
40: 0.04511, 39: 0.15645, 38:-0.21014, 37:-0.29784, 36: 0.08413, 35:-0.23893, 34:-0.35204, 33:-0.04780,
32:-0.26275, 31:-0.24951, 30:-0.22201, 29: 0.10042, 28: 0.01165, 27:-0.72590, 26: 0.21174, 25: 0.07463,
24:-0.27968, 23:-0.02771, 22:-0.21091, 21:-0.48833, 20:-0.14193, 19:-0.40699, 18: 0.06631, 17: 0.05779,
16:-0.71998, 15:-0.15625, 14:-0.60367, 13: 0.31168, 12:-0.25808, 11:-0.95552, 10: 0.05949, 9: 0.12759,
8:-0.03761, 7: 0.16631, 6: 0.22399, 5:-0.03790, 4: 0.49273, 3: 0.16559, 2:-0.10039, 1: 0.02486
605 | | 600:-0.32901, 599:-0.20505, 598: 0.03426, 597:-0.02329, 596:-0.25324, 595: 0.22039, 594:-0.08314, 593: 0.24280,
592:-0.06871, 591: 0.19590, 590: 0.17729, 589: 0.13549, 588:-0.22251, 587:-0.09548, 586:-0.08650, 585:-0.13550,
584: 0.00379, 583:-0.33000, 582: 0.08882, 581:-0.04712, 580: 0.34902, 579: 0.41748, 578: 0.12136, 577:-0.19793,
576: 0.07300, 575:-0.10725, 574:-0.25487, 573: 0.01133, 572: 0.36015, 571:-0.15085, 570:-0.48099, 569:-0.06718,
568:-0.24672, 567:-0.03002, 566: 0.09331, 565: 0.17252, 564: 0.21595, 563: 0.23324, 562: 0.10754, 561: 0.18805,
560:-0.25063, 559:-0.37146, 558: 0.29384, 557: 0.02669, 556: 0.06491, 555:-0.20653, 554:-0.04632, 553: 0.15222,
552:-0.10810, 551: 0.49974, 550:-0.23594, 549: 0.25607, 548: 0.37907, 547: 0.22339, 546: 0.43464, 545: 0.06852,
544: 0.38206, 543:-0.32527, 542: 0.24797, 541: 0.01122, 540: 0.09092, 539:-0.26130, 538: 0.02455, 537: 0.46487,
536:-0.01704, 535: 0.15221, 534:-0.25730, 533: 0.26293, 532: 0.18166, 531: 0.25888, 530:-0.22925, 529: 0.08345,
528: 0.15334, 527: 0.17251, 526:-0.30741, 525:-0.03522, 524: 0.02072, 523: 0.29387, 522: 0.19316, 521:-0.12227,
520: 0.12130, 519: 0.19589, 518: 0.20036, 517:-0.18279, 516:-0.12236, 515: 0.09839, 514: 0.07997, 513:-0.03236,
512:-0.33484, 511: 0.02707, 510: 0.46171, 509:-0.46403, 508:-0.02960, 507:-0.43948, 506:-0.34885, 505:-0.33046,
504:-0.41336, 503:-0.77513, 502:-0.31926, 501:-0.03536, 500: 0.25643, 499:-0.22968, 498:-0.27131, 497: 0.05796,
496:-0.01356, 495:-0.01239, 494:-0.11580, 493: 0.24628, 492: 0.27590, 491: 0.07271, 490:-0.01336, 489: 0.04921,
488:-0.11714, 487: 0.01754, 486: 0.16497, 485:-0.16841, 484:-0.19586, 483: 0.11162, 482:-0.18071, 481: 0.29592,
480: 0.22757, 479: 0.07272, 478: 0.37660, 477: 0.09794, 476: 0.05170, 475:-0.20540, 474: 0.00447, 473: 0.40152,
472: 0.48435, 471: 0.22464, 470:-0.14856, 469: 0.22416, 468:-0.25205, 467: 0.06519, 466:-0.17230, 465:-0.26762,
464:-0.26250, 463: 0.12502, 462:-0.35456, 461:-0.51144, 460: 0.03885, 459: 0.17529, 458: 0.02108, 457: 0.33443,
456: 0.47065, 455:-0.10627, 454:-0.08949, 453:-0.11765, 452:-0.11463, 451: 0.25247, 450: 0.10049, 449:-0.10399,
448: 0.14447, 447: 0.16968, 446:-0.23537, 445: 0.43178, 444: 0.11907, 443: 0.27883, 442:-0.10762, 441:-0.09433,
440:-0.32796, 439:-0.35972, 438:-0.38080, 437:-0.14401, 436: 0.40275, 435:-0.18055, 434:-0.31011, 433: 0.44213,
432: 0.10525, 431:-0.03075, 430: 0.38475, 429:-0.40923, 428: 0.13909, 427:-0.00420, 426:-0.28974, 425:-0.15888,
424: 0.63387, 423:-0.20712, 422:-0.37254, 421:-0.39954, 420:-0.23141, 419:-0.22303, 418: 0.13480, 417:-0.27742,
416: 0.18250, 415:-0.02993, 414:-0.04566, 413: 0.56137, 412: 0.06471, 411:-0.27712, 410: 0.09311, 409: 0.08268,
408: 0.17735, 407:-0.05491, 406:-0.28148, 405: 0.13570, 404:-0.37512, 403: 0.18594, 402:-0.11706, 401:-0.24715,
400:-0.04354, 399:-0.13444, 398:-0.57247, 397:-0.09582, 396: 0.07469, 395: 0.00195, 394: 0.07746, 393: 0.18315,
392:-0.07104, 391: 0.27859, 390:-0.23449, 389: 0.00568, 388: 0.17784, 387:-0.11555, 386:-0.07683, 385: 0.25961,
384:-0.26147, 383: 0.20808, 382:-0.13180, 381: 0.00097, 380: 0.00014, 379: 0.22099, 378:-0.10402, 377: 0.35027,
376:-0.04872, 375:-0.05759, 374:-0.10531, 373:-0.32392, 372: 0.22701, 371:-0.15486, 370: 0.01020, 369: 0.30405,
368: 0.22647, 367: 0.18415, 366:-0.09904, 365: 0.04266, 364: 0.24830, 363:-0.17874, 362: 0.09354, 361:-0.00262,
360: 0.11137, 359: 0.16644, 358: 0.13778, 357:-0.10013, 356:-0.45553, 355: 0.05888, 354:-0.16912, 353:-0.07327,
352:-0.08029, 351:-0.15828, 350:-0.19164, 349: 0.03242, 348: 0.13536, 347: 0.04412, 346:-0.04761, 345:-0.15623,
344: 0.07605, 343:-0.16057, 342: 0.12483, 341: 0.15619, 340:-0.28249, 339: 0.49599, 338: 0.25786, 337: 0.12555,
336: 0.07824, 335: 0.18339, 334: 0.04877, 333: 0.05908, 332:-0.21265, 331:-0.05555, 330:-0.23856, 329: 0.28631,
328:-0.14611, 327: 0.10530, 326:-0.00460, 325:-0.02502, 324: 0.14159, 323:-0.21405, 322:-0.29432, 321:-0.14651,
320: 0.05733, 319: 0.02302, 318: 0.13588, 317: 0.16717, 316: 0.15214, 315:-0.32689, 314: 0.19415, 313: 0.24979,
312: 0.24579, 311:-0.13405, 310:-0.33221, 309:-0.27822, 308:-0.07269, 307: 0.02739, 306: 0.17205, 305:-0.04820,
304: 0.01019, 303:-0.03461, 302: 0.02539, 301: 0.23290, 300:-0.08305, 299: 0.09336, 298:-0.08987, 297: 0.13111,
296: 0.31574, 295:-0.36948, 294:-0.24719, 293:-0.21805, 292:-0.28215, 291: 0.15411, 290:-0.06970, 289:-0.19370,
288:-0.07843, 287: 0.42215, 286: 0.07878, 285:-0.23104, 284:-0.26433, 283:-0.22623, 282: 0.32603, 281:-0.05373,
280: 0.06822, 279:-0.09293, 278:-0.10603, 277:-0.38168, 276: 0.42557, 275:-0.06534, 274:-0.39113, 273: 0.14655,
272: 0.00782, 271: 0.05031, 270:-0.11980, 269: 0.03598, 268: 0.35576, 267: 0.25536, 266:-0.22409, 265: 0.03411,
264:-0.29157, 263: 0.11383, 262:-0.04536, 261:-0.04744, 260:-0.22738, 259: 0.14478, 258: 0.40417, 257:-0.11771,
256:-0.23183, 255: 0.49590, 254: 0.02978, 253:-0.34416, 252:-0.36006, 251:-0.03785, 250: 0.21325, 249: 0.17068,
248:-0.31578, 247:-0.01812, 246:-0.02672, 245:-0.12386, 244: 0.21984, 243: 0.12418, 242:-0.37055, 241:-0.84740,
240: 0.18931, 239: 0.00065, 238:-0.22013, 237: 0.07617, 236: 0.03472, 235: 0.24355, 234: 0.24192, 233: 0.27602,
232:-0.06698, 231:-0.74109, 230: 0.01075, 229:-0.08024, 228:-0.04741, 227: 0.27760, 226:-0.02562, 225: 0.20316,
224:-0.02452, 223:-0.35036, 222: 0.04349, 221:-0.53228, 220:-0.00956, 219: 0.04814, 218:-0.29294, 217: 0.20631,
216: 0.15673, 215:-0.16583, 214: 0.03952, 213:-0.29387, 212: 0.17831, 211:-0.15417, 210: 0.06804, 209:-0.00574,
208: 0.06693, 207: 0.09607, 206:-0.00108, 205: 0.28158, 204:-0.27128, 203:-0.15411, 202: 0.11728, 201: 0.24387,
200: 0.18051, 199:-0.20685, 198:-0.02205, 197: 0.13229, 196: 0.22004, 195: 0.14643, 194:-0.06058, 193:-0.11559,
192:-0.18019, 191: 0.35745, 190:-0.29565, 189: 0.18979, 188:-0.11939, 187:-0.26806, 186:-0.06722, 185:-0.45620,
184:-0.14505, 183: 0.11742, 182: 0.00392, 181: 0.44147, 180:-0.28157, 179:-0.05367, 178:-0.25158, 177: 0.00803,
176:-0.32224, 175:-0.08313, 174:-0.16214, 173:-0.11599, 172: 0.18053, 171:-0.31758, 170:-0.37970, 169: 0.13194,
168:-0.33397, 167:-0.20842, 166: 0.05291, 165:-0.01408, 164:-0.30133, 163:-0.18141, 162:-0.16360, 161: 0.17523,
160: 0.05845, 159: 0.26657, 158:-0.01631, 157: 0.38329, 156: 0.15814, 155:-0.07443, 154:-0.10423, 153:-0.24469,
152:-0.18059, 151: 0.06716, 150: 0.17718, 149: 0.13133, 148: 0.22000, 147: 0.00323, 146:-0.20053, 145:-0.00306,
144:-0.12677, 143:-0.06679, 142: 0.18823, 141: 0.23318, 140: 0.02743, 139: 0.28056, 138:-0.18256, 137:-0.08768,
136:-0.19559, 135: 0.15040, 134:-0.27681, 133:-0.13469, 132: 0.14161, 131:-0.15445, 130:-0.01133, 129: 0.03761,
128: 0.18891, 127:-0.07532, 126: 0.12125, 125: 0.11565, 124: 0.22430, 123: 0.22008, 122:-0.08070, 121:-0.18887,
120:-0.13159, 119: 0.26815, 118: 0.14090, 117:-0.07888, 116:-0.11229, 115:-0.15217, 114:-0.06658, 113: 0.15482,
112:-0.13988, 111: 0.50768, 110: 0.29590, 109: 0.10986, 108: 0.05922, 107: 0.19017, 106: 0.25759, 105: 0.10858,
104: 0.12594, 103:-0.29317, 102:-0.34908, 101:-0.30159, 100: 0.33526, 99:-0.04471, 98: 0.43860, 97:-0.10559,
96: 0.19658, 95:-0.31246, 94: 0.41989, 93:-0.35018, 92:-0.08708, 91: 0.05431, 90:-0.33252, 89:-0.02753,
88: 0.04044, 87: 0.26833, 86:-0.34337, 85: 0.29855, 84:-0.32477, 83:-0.02032, 82:-0.06057, 81:-0.11322,
80: 0.02794, 79:-0.03900, 78:-0.21132, 77:-0.13260, 76: 0.18201, 75:-0.25694, 74:-0.23131, 73:-0.29685,
72: 0.19498, 71: 0.08130, 70:-0.04146, 69:-0.04448, 68: 0.32396, 67: 0.03568, 66: 0.15865, 65:-0.34358,
64:-0.19802, 63: 0.46185, 62:-0.18292, 61:-0.38272, 60: 0.01987, 59:-0.11336, 58: 0.11684, 57:-0.26052,
56:-0.27765, 55: 0.33572, 54:-0.17565, 53: 1.04222, 52:-0.13083, 51: 0.03533, 50:-0.10124, 49: 0.42525,
48: 0.14862, 47: 0.01310, 46: 0.27100, 45:-0.25615, 44: 0.29663, 43: 0.04623, 42:-0.06321, 41:-0.05596,
40: 0.14647, 39:-0.04754, 38: 0.27204, 37:-0.08451, 36:-0.26646, 35:-0.27756, 34: 0.10655, 33: 0.11385,
32:-0.01991, 31: 0.11086, 30:-0.02755, 29:-0.28642, 28:-0.32812, 27: 0.01069, 26: 0.12208, 25:-0.03904,
24: 0.32555, 23:-0.23981, 22: 0.19537, 21:-0.07039, 20:-0.13281, 19:-0.16505, 18:-0.05692, 17:-0.01968,
16: 0.06834, 15:-0.22804, 14:-0.15798, 13:-0.00184, 12:-0.15475, 11: 0.31434, 10:-0.19855, 9:-0.26497,
8: 0.28706, 7: 0.06602, 6: 0.22902, 5:-0.13532, 4:-0.21723, 3: 0.06886, 2:-0.24007, 1: 0.30851
606 | | 605:-2.01494, 604: 4.32118, 603: 1.05604, 602:-2.60890, 601: 1.65098
-------|------|----------------------------------------------------------------------------------------------------------------
predictions <- predict(model,test_set.norm.X)
mlpendtime <- Sys.time()
predictions <- predictions * (maxteStr_h_texture - minteStr_h_texture)
predictions <- round(predictions,0)
mlptable <- table(test_set$Str_h_texture,predictions)
mlprow <-rownames(mlptable)
mlpcol <- colnames(mlptable)
mlpscore <- sumElementinTable(mlptable,mlprow,mlpcol)/sum(mlptable)
mlptakentime <- mlpendtime - mlpstarttime
cat('The score of MLP is ', mlpscore,'\n')
The score of MLP is 0.02635098
cat('It takes ', mlptakentime,'seconds')
It takes 58.56548 seconds
We can use neuralnet() to train a NN model. Also, the train() function from caret can help us tune parameters. We can plot the result to see which set of parameters is fit our data the best.
tuning parameter
Model <- train(Str_h_texture ~ .,
data=train_set,
method="neuralnet",
### Parameters for layers
tuneGrid = expand.grid(.layer1=c(1:2), .layer2=c(0:2), .layer3=c(0)),
### Parameters for optmization
learningrate = 0.01,
threshold = 0.01,
stepmax = 5000
)
in nnclassifier y value should be normalized
train_set.norm <- train_set
maxStr_h_texture <- max(train_set.norm$Str_h_texture)
minStr_h_texture <- min(train_set.norm$Str_h_texture)
train_set.norm$Str_h_texture <- normalize(train_set.norm$Str_h_texture)
nnClassifier <- neuralnet(Str_h_texture ~ .,data=train_set.norm, likelihood = TRUE,
hidden = 1,linear.output = F,act.fct = "tanh")
print(nnClassifier$result.matrix)
plot(nnClassifier)
prediction
output<- compute(nnClassifier,train_set[,-1])
p1 <- output$net.result
p1 <- p1 * (maxStr_h_texture-minStr_h_texture)
p1 <- round(p1,0)
nntable<- table(train_set$Str_h_texture,p1)
Xgboost can work perfectly in sparse matrix but it unfortunately cannot run in 5 hours
xgb.train = xgb.DMatrix(data = as.matrix(train_set),label =as.matrix(train_set$Str_h_texture))
xgb.test = xgb.DMatrix(data = as.matrix(test_set),label = as.matrix(test_set$Str_h_texture))
validsoilTexture$Str_h_texture <- as.factor(validsoilTexture$Str_h_texture)
num_class = length(levels(validsoilTexture$Str_h_texture))
params = list(
booster="gbtree",
eta=0.001,
max_depth=5,
gamma=3,
subsample=0.75,
colsample_bytree=1,
objective="multi:softprob",
eval_metric="mlogloss",
num_class=num_class+1
)
# Train the XGBoost classifer
xgb.fit=xgb.train(
params=params,
data=xgb.train,
nrounds=10000,
nthreads=1,
early_stopping_rounds=10,
watchlist=list(val1=xgb.train,val2=xgb.test),
verbose=0
)
xgb.fit
Random Forest* The algorithm cannot run successfully since it will give an Error: cannot allocate vector of size 16.5 Gb random forest is bad for sparse data which can be found in https://stats.stackexchange.com/questions/28828/is-there-a-random-forest-implementation-that-works-well-with-very-sparse-data
RfClassifier = randomForest(Str_h_texture ~ .,data = train_set,proximity = T,mtry = 10)
rfTable <- table(predict(RfClassifier),train_set$Str_h_texture)
print(RfClassifier)
plot(RfClassifier)